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Record W4244434490 · doi:10.1503/cjs.004018

Trauma Association of Canada Abstracts 2018

2018· article· en· W4244434490 on OpenAlexaffvenueabout
M. Azam Majeed, Peter J. Gill, Mboutidem Etokakpan, Sherry MacGillivray, Deepak Choudhary, Nathalie Rodrigue, Shankar Haran, Joel Lockwood, Pamela Fuselli, Osaree Akaraborworn, Abigail Tien, Jameel Ali, Farah Ladak, Stéphanie Leclerc, Brandon Batey, Yaser Selim, Recep Gezer, Joseph Margolick, Ian Watson, Susan Benjamin, Bryn Robinson, Abdullah Albarrak, David Bracco

Bibliographic record

VenueCanadian Journal of Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsVancouver General HospitalUniversité LavalUniversity of British ColumbiaAlberta Hospital EdmontonParachuteUniversity of TorontoMcGill UniversityMcGill University Health CentreUniversity of Alberta HospitalAlberta Children's HospitalLondon Health Sciences CentreAlberta Health ServicesHospital for Sick Children
FundersBritish Heart Foundation
KeywordsMedicineMortality rateEmergency medicineInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Background: Traumatic cardiac arrest (TCA) carries high mortality and morbidity.Survival is poor.Recent studies suggest the rate of morbidity and mortality due to TCA is approaching the same as that due to any other cause of cardiac arrest.The common causes are head trauma, tension pneumothorax, spinal in juries and hypovolemia.The commonly found rhythm in TCA is pulseless electrical activity (PEA) followed by asystole and then ventricular fibrillation (VF).This study identified survival rates and factors affecting them.Methods: A 5-year retrospective database review was conducted to identify trauma patients who had traumatic cardiac arrest.This study was conducted at the University Hospitals Birmingham, England, which is a Level 1 regional trauma centre.The primary outcome measure was survival to hospital discharge.The secondary outcome was to look at the factors affecting survival.Results: Forty patients had out-of-hospital cardiac arrest secondary to major trauma.The mean age was 50 years (16-84 years) and the male to female ratio was 32:8.The commonest mechanism involved was road traffic accidents and then falls.The mean injury severity score (ISS) was 37.5 (9-66) and the most commonly injured regions were as follows: chest 70% (28 patients), head 55% (22), face 40% (16), spine 37% (15), abdomen 27% (11) and 15% (6) with pelvic injuries.Ninety-five percent (38 out of 40) of patients received adrenaline and all (100%) patients received CPR.Out of 40, only 60% (24 patients) received blood.The mean length of CPR was 46 minutes (2-90 minutes).Only 55% ( 22) patients got return of spontaneous circulation on scene and 35% (14) more patients while in the resus room.The commonest rhythm was asystole 78% (31 patients), PEA 18% (7 patients) and then 5% (2 patients) had VF.Only 4 patients (10%) survived to discharge.When we looked at these alive patients they had received CPR for a mean time of 4.5 minutes (2-7 minutes), and their mean age was 39 years (31-48 years).Among these 4 patients, 2 had sustained only rib fractures and lung contusions.One patient had renal contusion and liver laceration.The last patient was a case of drowning.Conclusion: The survival rates described are poor but comparable with (or better than) published survival rates for out-of-hospital cardiac arrest of any cause.Patients who have active bleeding on scene leading to hypovolemia have a poor chance of survival.Also, the survivors had a very short time of cardiopulmonary resuscitation.Most of the guidelines suggest a poor chance of survival with longer resuscitation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.843
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.7540.501

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.228
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes3
Has abstractyes

Explore more

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