Emergency Medicine

Delay to initiation of out-of-hospital cardiac arrest EMS treatments

a b s t r a c t

Background: Time to initial treatment is important in any response to out-of-hospital cardiac arrest (OHCA). The purpose of this paper was to quantify the time delay for providing initial EMS treatments supplemented by com- parison with those of other EMS systems conducting clinical trials.

Methods: Data were collected between 1/1/16-2/15/19. Dispatched, EMS-worked, adult OHCA cases occurring before EMS arrival were included and compared with published treatment time data.

Response time and time-to-treatment intervals were profiled in both groups. Time intervals were calculated by subtracting the following timepoints from 9-1-1 call receipt: ambulance in route; at curb; patient contact; first defibrillation; first epinephrine; and first antiarrhythmic.

Results: 342 subjects met study inclusion/exclusion. Mean time intervals (min [95%CI]) from 9-1-1 call receipt to the following EMS endpoints were: dispatch 0.1 [0.05-0.2]; at curb 5.0 [4.5, 5.5]; at patient 6.7 [6.1, 7.2];, first de- fibrillation initially shockable 11.7 [10.1, 13.3]; first epinephrine (initially shockable 15.0 [12.8, 17.2], initially

non-shockable 14.8 [13.5, 15.9]), first antiarrhythmic 25.1 [22.0, 28.2]. These findings were similar to data in 5 published clinical trials involving 12,954 subjects.

Conclusions: Delay to EMS treatments are common and may affect clinical outcomes. Neither Utstein out-of-hos- pital guidelines [1] nor U.S. Cardiac Arrest Registry to Enhance Survival (CARES) databases require capture of these elements. EMS is often not providing treatments quickly enough to optimize clinical outcomes. Further reg- ulatory change/research are needed to determine whether OHCA outcome can be improved by novel changes such as enhancing bystander effectiveness through drone-delivered drugs/devices & real-time dispatcher direc- tion on their use.

(C) 2020

  1. Introduction

Approximately 395,000 people per year die in the United States (U.S.) due to out-of-hospital cardiac arrest (OHCA) [1,2]. Despite recent improvements in community preparedness and EMS response, such as dispatcher-instructed “Hands-only CPR“, layperson CPR training, avail- ability of automated external defibrillators, and first responder/EMS care, survival rates in most communities remain relatively flat [3].

Time to initial treatment is an important element in any response to cardiac arrest. For example, Early defibrillation [4,5], epinephrine [6-11], and amiodarone [12,13] are associated with improved outcomes. The incidence of ventricular fibrillation, a shockable rhythm associated with higher survival, decreases by 2% for every minute delay from

* Corresponding author at: 9813 Ridge Meadow Place, Henrico, VA 23238, United States of America.

E-mail address: [email protected] (J.P. Ornato).

9-1-1 call receipt until monitor application [4] and the odds of survival decrease by 10% for each passing minute until defibrillation [5].

Implementation of the Chain of survival paradigm is mostly linear, consisting of bystander discovery of a victim in cardiac arrest, ideally with initiation of CPR and a prompt call to 9-1-1. The dispatcher will then dispatch First responders/EMS personnel to the scene as well as directing the bystander to initiate/continue CPR and retrieve/apply an automated external defibrillator if one is readily available. Once re- sponders arrive at the curb, they must find the patient, confirm the car- diac arrest, initiate or take over CPR, apply a monitor defibrillator/ deliver a shock when indicated, start an IV, and administer medication. Significant delays are inherent with one or more of these steps even in the most efficient EMS systems [12-16].

How common are delays attributable to the EMS response to treat- ment of cardiac arrest victims and are our current data systems record- ing all of the necessary time elements for EMS treatments known to impact outcomes such as defibrillation and drug therapy? The purpose of this paper was to quantify the time delay sequence to initial EMS

https://doi.org/10.1016/j.ajem.2020.12.024

0735-6757/(C) 2020

treatments and compare these delays to similar data from other EMS systems participating in large, randomized, out-of-hospital, published, adult cardiac arrest clinical trials.

  1. Methods
    1. EMS system characteristics

Data for this study were collected from EMS. The Department of Fire “first response” assistance on life or death emergency calls using fire ap- paratus based at twenty fire stations. Fire trucks are staffed by emer- gency medical technicians (EMTs) who can perform Manual CPR and defibrillate using Automated external defibrillators . Median time interval from 9-1-1 call receipt until curb arrival (commonly known as the “first-responder response time interval“) is 5 min. The city’s EMS agency provides 9-1-1 dispatched EMS ambulance service for the city (population 230,436; service area 62.5 mile2) primarily serv- ing an urban, Inner city environment. EMS ambulances are deployed using an Advanced system status management strategy. EMS’s median response time interval to the curb for cardiac arrest cases is also 5 min with both first response and EMS ambulance units arriving within 30s of each other on >75% of calls. The time of initial defibrillation was taken as the first shock administered by the first responder AED or ALS ambulance defibrillator. Intravenous epinephrine is the sole vaso- constrictor and amiodarone is the only antiarrhythmic drug used to treat patients with OHCA in the city.

Dispatch data (including curb and patient arrival) are stored digitally in the computer aided dispatch (CAD) system (Intergraph/Hexagon Safety & Infrastructure, Madison, AL). All EMS system timepieces are

synchronized to the atomic clock accurate to one second. EMS crews no- tify dispatch by radio when they make “patient contact” and the dis- patcher immediately records the data into the CAD system. Time of EMS system defibrillation was captured digitally on the EMS monitor defibrillator (X-series, ZOLL(R) Medical Corp., Chelmsford, MA) or fire first responder AED (same X-series device used in AED mode). Providers had a choice of entering the time of epinephrine or amiodarone admin- istration by pushing a button on their monitor defibrillator during drug injection or keeping a written flowsheet and transferring the time data into the electronic patient care record (ZOLL(R) EMScharts(R), Broomfield, CO), after the call.

    1. Study population

The initial data collection included all 860 adult, EMS-worked, OHCA patients in the city between 1/1/16-2/15/19. Only adult cases dispatched with a principal determinant of cardiac arrest were included. Cases in which the event did not occur after EMS arrival were excluded (Fig. 1). We also excluded all cases eventually found to be a cardiac ar- rest but not dispatched as such because of the information provided by the caller. Inclusion of such cases would likely have lengthened the response time intervals for some of the categories (e.g., shooting, stab- bing, assault in which EMS units “stage” several blocks away from the victim until law enforcement officers “clear the scene”). In other cases, caller’s complaint may or may not have required a Priority 1 dispatch with fire first response. We chose to focus on only a homogeneous group of cases meeting dispatch criteria for cardiac arrest which gener- ated an EMS and fire “lights and sirens” response.

Image of Fig. 1

Fig. 1. Description of study population.

    1. Study design and outcome measures

EMS time intervals were calculated by subtracting the following timepoints on each call from 9-1-1 call receipt (or obtained from pub- lished clinical trials in the comparison group): ambulance in route; at curb location; patient contact; first defibrillation for cases with an ini- tially shockable rhythm; first epinephrine and first antiarrhythmic administration.

Study site EMS adult cardiac arrest call response time intervals were profiled against those of EMS agencies involved in several published contemporary, randomized, OHCA clinical trials that collected/reported time interval treatment data [12-16].

    1. Data analysis

Data were kept in a secure REDCAP database. IBM SPSS Statistics Version 27 was used for data analysis. Fisher’s Exact test (2-tailed) was used for comparison of discrete variables. Student’s t-test (2-tailed) was used to compare the time interval to epinephrine and antiarrhyth- mic drug administration based the method of data collection (monitor defibrillator vs. written flowsheet).

    1. Ethical considerations

EMS study site data was deidentified prior to delivery to the investi- gators for analysis. The Committee for the Conduct of Human Research reviewed and approved the study.

  1. Results

A total of 342 subjects met study Inclusion/exclusion criteria (Fig. 1). Details of the EMS study site population (Table 1) were characteristic of those found in many U.S. inner-city, urban EMS systems. An initially shockable rhythm was present twice as frequently for witnessed vs. unwitnessed arrest cases (p = .03). Return of spontaneous circulation occurred more frequently when the arrest was witnessed (p = .001).

All time data were captured electronically by either the CAD system or monitor defibrillator except for: 1) the time of epinephrine adminis- tration which was documented by a button push on the monitor defi- brillator (42.1%) or written flowsheet with transfer to the ePCR after the call (57.9%) and 2) the time of antiarrhythmic drug administration which was documented by a button push on the monitor defibrillator (45.7%) or written flowsheet with transfer to the ePCR after the call (54.3%). There was no statistically significant difference in the time in- terval from 9-1-1 call receipt to epinephrine administration based the method of data collection (monitor defibrillator = 15.6 [14.7, 16.4] min; written flowsheet = 15.1 [14.5, 15.8] min; p = .41). There

was no statistically significant difference in the time interval from 9-1-1 call receipt to amiodarone administration based the method of data collection (monitor defibrillator = 27.7 [21.4, 33.9] min; written flowsheet = 23.0 [20.3, 25.6] min; p = .14). There was no statistically significant difference in the presence of an initially shockable rhythm or ROSC between groups in which an AED was or was not applied, both for unwitnessed and witnessed arrests. The similarity in outcome measures between groups is likely because most (129/157, 82%) of the 45.9% of calls in which an AED was applied prior to EMS arrival had the device applied by a fire first responder who, in our EMS system frequently arrive almost simultaneously (within 30s of each other 75% of the time) as the EMS crew.

Table 2 compares time intervals from 9-1-1 dispatch at the EMS study site vs. those documented in several recent publications in which EMS crews were involved in randomized OHCA clinical trials [9-13]. Although not all time intervals were reported across the various groups and there was no uniformity in the reporting method (i.e., some of the publications reported values as mean, 95% confidence intervals [CI], and/or ranges), the table paints a similar picture between the EMS study site and the comparison EMS system data. Each of these groups and time interval endpoints show a substantial delay between the EMS response time interval (9-1-1 dispatch to “at curb”) vs. onset of initial EMS treatments such as defibrillation, epinephrine, or antiar- rhythmic administration.

Table 3 compares data elements included in the latest Utstein OHCA guidelines [2] and the U.S. Cardiac Arrest Registry to Enhance Survival (CARES) [1]. Neither of these require capture of all such elements for OHCA events. Time of first patient contact and first defibrillation is a Supplemental Element in CARES. Utstein out-of-hospital guidelines in- clude time of first defibrillation but neither time of epinephrine nor an- tiarrhythmic collection. Time to first epinephrine and antiarrhythmic administration have been added to the 2019 Utstein in-hospital cardiac arrest guideline [17].

  1. Discussion

The principal finding of this study is that significant time delays to initial treatments exist among the data collected by EMS and values depicted in the cited literature. Collectively, these data lay bare the ob- vious, which is that the typical “response time intervals” which EMS agencies detail in internal quality reports and clinical trial publications are considerably shorter than the time intervals from 9-1-1 call receipt until initial treatments. The lack of uniformity in reporting such endpoints in clinical trials is noteworthy and uncovers a critical omission. Thus, there is an opportunity for uniform cardiac arrest data collection and reporting to include more physiologically relevant

Table 1

EMS study site population characteristics.

All cases

p-value

No AED applied

AED applied

p-value

Total number of cases

342

185 (54.1%)

157 (45.9%)

Age [years +-95% CI]

60.4 [58.2, 62.6]

61.4 [58.3, 64.5]

59.3 [56.2, 62.4]

0.35

Male sex

204 (59.6%)

105 (51.5%)

99 (48.5%)

0.27

Witnessed arrest

122 (35.7%)

62 (50.8%)

60 (49.2%)

0.37

Bystander CPR

120 (35.1%)

77 (64.2%)

43 (35.8%)

0.01

AED application prior to ALS crew arrival

Fire first responder

129 (37.7%)

0.03

Layperson

4 (1.2%)

Non-dispatched healthcare provider or EMS bystander 24 (7.0%)

No AED application prior to ALS arrival at patient 185 (54.1%)

Initial shockable rhythm

31 (9.1%)

20 (10.8%)

11 (7.0%)

0.26

Unwitnessed arrest

14 (6.4%)

0.03

9 (7.3%)

5 (5.2%)

0.59

Witnessed arrest

17 (13.9%)

11 (17.7%

6 (10.0%)

0.30

Return of spontaneous circulation (ROSC)

90 (26.3%)

44 (23.8%)

46 (29.3%)

0.27

Unwitnessed arrest

45 (20.5%)

0.001

22 (17.9%)

23 (23.7%)

0.32

Witnessed arrest

45 (36.9%)

22 (35.5%)

23 (38.3%)

0.85

Table 2

Time intervals from 9-1-1 dispatch at the EMS study site compared with those documented in several recent publications in which EMS crews were involved in randomized OHCA clinical trials.

First defibrillation First epinephrine

# EMS

agencies

#

subjects

Dispatch

At curb

At patient

Initially shockable

Initially

non-shockable

Initially shockable

Initially

non-shockable

First antiarrhythmic

aRAA (all cases)

1

342

0.1 [0.05,

5.0 [4.5,

6.7 [6.1,

11.7 [10.1,

24.9 [22.4,

15.0 [12.8,

14.7 [13.5,

25.1 [22.0,

0.2]

5.5]

7.2]

13.3]

27.3]

17.2]

15.9]

28.2]

aRAA (no AED applied before

185

0.1 [0,

4.2 [3.6,

5.9 [5.2,

11.0 [9.0,

26.1 [22.6,

15.0 [12.2,

14.5 [13.8,

26.6 [21.9,

ALS arrival)

(54.1%)

0.2]

4.9]

6.5]

13.0]

29.6]

17.7]

15.2]

31.3]

aRAA (AED applied before ALS

157

0.2 [0,

6.3 [5.6,

8.1 [7.3,

13.1 [10.0,

23.0 [19.6,

15.2 [12.7,

16.3 [15.5,

23.7 [18.3,

arrival)

(45.9%)

0.4]

7.1]

8.8]

16.2]

26.4]

17.7]

17.0]

29.1]

bALPS initially shockable [10]

55

3026

Not

5.8-6.3

Not

Not

Not

15.8-16.2

Not

19.3

reported

(BLS)

reported

reported

reported

reported

7.8-8.0

bALPS initially non-shockable

55

1063

Not

(ALS)

6.0-6.3

Not

Not

20.4-21.1

Not

Not

27.1-28.1

[12]

reported

(first

reported

reported

reported

reported

cPARAMEDIC2 [13]

5

8014

Not

EMS)

7.4

Not

Not

Not

Not

22.6

Not

reported

reported

reported

reported

reported

reported

bARREST [11]

5

504

Not

4.3-4.4

Not

8.9-9.5

Not

Not

Not

20.5-21.4

reported

[BLS]

reported

reported

reported

reported

8.4-8.8

cALIVE [9]

1

347

Not

[ALS]

Not

7.3

8-9

Not

Not

Not

24-25

reported

reported

reported

reported

reported

Table 3

Comparison of data elements included in the latest Utstein OHCA guidelines [2] and the

U.S. Cardiac Arrest Registry to Enhance Survival (CARES) [1]

Time elements Utstein CARES

the more clinically relevant “biological Ischemic time interval” from ar- rest onset until sustained ROSC.

Recording and analyzing the time intervals from 9-1-1 call receipt to initiation of EMS interventions is necessary to define the “time win-

Required elements

Required & supplemental elements

dows” in which treatments are beneficial. For example, analysis of

epinephrine’s role in OHCA management without respect to the timing of Initial administration reveals consistent improvement in ROSC and

Estimated time of arrest No No No

9-1-1 call receipt No Yes

Dispatch Yes No Yes

hospital/intensive care unit admission but minimally different or worse survival to hospital discharge and/or neurological impairment compared to placebo [16,18-21]. However, when the time interval to initiation of

Curb arrival Yes (1st response

vehicle)

No Yes

epinephrine administration is considered, several clinical studies have

Patient contact No No Yes

First defibrillation Yes No Yes

First epinephrine No No No

First antiarrhythmic No No No

Sustained ROSC No No No

interventions that, when administered early, have the potential to affect outcomes.

The time interval from onset of a critical biological event (e.g., major trauma, acute myocardial infarction, cardiac arrest, stroke) to initiation of definitive treatment(s) is often the determinant of outcome and is expressed in such phrases as the “golden hour”, “time is muscle”, or “time is brain”. For OHCA victims, published clinical trials commonly re- port the “EMS response time interval”, often differentiating first re- sponder BLS vs EMS arrival endpoints. But these endpoints report, with rare exception, arrival to the “curb” location rather than the deliv- ery of time dependent interventions. Results from the current analysis illustrate the substantial additional time delays that are infrequently re- corded or published [12-16], likely because they are perceived to be dif- ficult to capture during patient care and are not required data elements in the CARES [1] or out-of-hospital Utstein Guidelines [2]. In addition, none of these time points or intervals include the unmeasurable delay from onset of the event/bystander discovery of the victim and 9-1-1 call receipt. Inclusion of such a time point is rarely determinable unless the event occurs under video surveillance [5] but is highly relevant to

documented improved neurologically-intact survival to hospital dis- charge with early epinephrine [6-11]. Of particular note is a CARES regis- try analysis of 2100 out-of-hospital, witnessed, non-traumatic adult cardiac arrest cases in North Carolina showing early vasopressor (92% epi- nephrine, 8% vasopressin) administration is associated significantly with Neurologically intact survival with the odds of a CPC 1-2 outcome declin- ing 10% for every one-minute delay from 9-1-1 call receipt [9]. The Resus- citation Outcomes Consortium (ROC) Amiodarone, Lidocaine, or Placebo (ALPS) trial showed benefit of antiarrhythmic treatment for ventricular fibrillation refractory to initial defibrillation attempts only for witnessed OHCA cases, suggesting that there was likely no benefit for unwitnessed cases because of a more prolonged time interval to treatment due to the additional time delay before a bystander discovered the victim [12].

Why do current out-of-hospital Utstein and CARES guidelines not re- quire routine data collection and reporting of these additional time ele- ments? They are obviously more challenging for EMS personnel to collect/document as they are focusing their efforts on providing treat- ment. However, it is now technologically possible for an increasing number of EMS systems using atomic clock-synchronized, monitor/ defibrillators to capture these data in real-time by pushing a button on the device with minimal disruption of their resuscitation activities. For such EMS systems, it is just a matter of training and cultural change to enable standard capture of these critical time elements that can affect outcome. EMS data for documenting the time to epinephrine and anti- arrhythmic administration show that the two methods of data capture (button push or written flowsheet) yield statistically similar results (al- though this study was not designed as a direct comparison of the two), so it may not be essential for EMS agencies who do not have monitor

defibrillators that can capture these elements to bear the expense of purchasing such newer devices. Further study is necessary to better de- fine the correlation between these methods.

The long delays to initiation of prehospital EMS cardiac arrest treat- ments is likely a relevant factor (along with obviously different patient characteristics) explaining why survival to hospital discharge (espe- cially with a good neurologic function) is so much higher for in- vs. OHCA patients. In the U.S., survival to hospital discharge occurs in approximately 10% of EMS-worked arrests [3], but these cases represent only half of all events (the remainder are dead on EMS arrival to the scene and not worked), resulting in an actual survival rate of about 5-6%. In contrast, survival to hospital discharge was 25.8% of 26,742 adult in-hospital cardiac arrests at 319 U.S. hospitals participating in the American Heart Association’s Get-with-the- Guidelines-Resuscitation data registry [3]. Unadjusted survival rate after in-hospital cardiac arrest was 18.4% in the UK National Cardiac Arrest Audit database between 2011 and 13 [22].

    1. Implications for the enhancement of lifesaving EMS service delivery

How can EMS agencies further shorten the time to OHCA treat- ments? Dramatically increasing bystander defibrillation with an AED is an obvious initial opportunity. The Public access defibrillation trial [23] showed the benefit and safety of bystander defibrilla- tion with an AED, especially for events occurring in public places where a modest number of devices can be pre-positioned to protect a large number of potentially “at-risk” individuals [24]. However, this strategy protects only a small percentage of cardiac arrest patients. From 2011 to 15, only 8.3% of 49,555 patients in the ROC clinical trials had a witnessed OHCA in a public setting and only 1% received a by- stander shock with an AED [25]. Fewer than 2% of EMS-worked OHCA patients in the ROC and CARES database from 2015 to 18 received a by- stander shock using an AED, even though the odds of neurologically in- tact survival increased almost 3-fold when a bystander delivered a shock with an AED compared to awaiting EMS arrival [3].

Several EMS agencies are exploring the use of unmanned aerial vehicles (aka, “drones”) to deliver AEDs and other emergency treatments to bystanders at the scene of a life-threatening medical emergency [26-30]. Such a strategy has the potential to dramatically increase by- stander AED use at a more affordable cost for communities [31-37]. As an example, ourcity would have to pre-position 7750 AEDs within its borders at a startup cost of almost $12 million to achieve a goal of having 9-1-1 dispatchers direct a bystander to fetch and administer treatment with a pre-positioned AED within 2 min as EMS is responding to the call [29]. In contrast, it would take only six commercial drones to deliver the devices to the bystander in <2 min at a cost of <$60,000. For this location, the drone solution is roughly 200 times less expensive than prepositioning AEDs throughout the community.

Until recently, Federal Aviation Administration (FAA)/European Avi- ation Safety Agency (EASA) regulations have been the major obstacle preventing widespread drone delivery. Ongoing modification of com- mercial drone regulations and procedures [38,39] are proceeding at a rapid pace in the U.S. with recent approval of package delivery for com- panies such as Google Wing [40], Federal Express (FedEx) [41], Amazon [42], and United Parcel Service (UPS) [43]. Such developments are likely to soon allow drone-delivery of medical devices, medications, and sup- plies to bystanders who, under emergency dispatcher direction, can more frequently begin early Life-saving interventions while EMS first- responders and EMS personnel are racing to the victim.

    1. Limitations

A limitation of this study is that its original data are from a single, rel- atively fast-responding EMS system with electronic infrastructure to collect time-sensitive data. Data in Table 2 confirm that other EMS

systems involved in out-of-hospital, randomized, clinical drug or device clinical trials in adults have very similar time intervals to initial treat- ments understanding that a small amount of addition time could theo- retically have been expended in carrying out clinical trial procedures (e.g., screening, case randomization, experimental treatment kit). The fact that all published time data cited in this manuscript are from EMS systems that are involved in clinical trials could affect the generalizabil- ity of these results to systems without these characteristics. However, if anything, one might expect that EMS systems involved in clinical re- search might represent a higher level of training and competence of its providers compared to systems not involved in such endeavors. Also, the EMS service areas of each of systems included for analysis are mainly urban and suburban, making it inappropriate to generalize these results to EMS systems serving rural and remote regions where times to treatment are likely even longer.

  1. Conclusions

Time is the enemy when it comes to treating OHCA. Delays from 9-1-1 call receipt to administration of initial EMS treatments are common and have been shown to impact outcomes. Neither the latest 2015 Utstein guidelines [2] nor the U.S. Cardiac Arrest Registry to Enhance Survival (CARES) [1] require capture of all such elements for OHCA events although time to first epinephrine and antiarrhythmic administration have been added to the 2019 Utstein in-hospital cardiac arrest guide- line [17] and are required in the American Heart Association’s Get- with-the-Guidelines Resuscitation [44] in-hospital database. CARES pro- vides the opportunity to input optional dispatch, scene time, and first defibrillation/epinephrine use. Addition of such elements to out-of- hospital data collection requirements appears justified. In addition to pro- viding an important quality improvement tool, Standardized reporting of these data elements in clinical trials could better define the time-to-treat- ment effect of interventions (e.g., epinephrine) on outcomes.

EMS is often not providing treatments quickly enough to optimize clinical outcomes. Further regulatory change and research is needed to determine whether OHCA outcome can benefit from novel changes such as enhancing bystander effectiveness through drone-delivered drugs/devices & real-time dispatcher direction on their use.

Financial disclosure

Dr. Ornato receives compensation as Operational Medical Director for RAA and Richmond Fire & EMS. Dr. Peberdy is married to Dr. Ornato but has no other financial disclosures. Mr. Lindfors, Ludin, and Garrison are employees of RAA. Mr. Siegel is a medical student and has no finan- cial disclosures to report.

Funding source

No funding was received for the data collection, analysis, or prepara- tion of the manuscript. Dr. Ornato is Principal Investigator on a National Institutes of Health/National Institute of Drug Abuse grant (1R41DA051293-01) entitled Drone-Delivered Naloxone System for opioid overdose Treatment (active dates 7/1/20-6/29/21).

Author contributions

RAA employees Rich Lindfors, Tom Ludin, and Danny Garrison were responsible for collecting and providing the data for analysis.

Joseph P. Ornato, Mary Ann Peberdy, and Charles R. Siegel at Virginia Commonwealth University were primarily responsible for ana- lyzing the data and writing the initial draft manuscript as well as its revisions.

All co-authors had input into the initial draft and revisions of the manuscript.

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