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Record W4220787124 · doi:10.21203/rs.3.rs-1412016/v1

Risk factors and mortality associated with undertriage after major trauma in a physician- led prehospital system: a retrospective multicentre cohort study

2022· preprint· en· W4220787124 on OpenAlexaff
Axel Benhamed, Laurie Fraticelli, Clément Claustre, E. Cesaréo, Amaury Gossiome, Matthieu Heidet, Marcel Émond, Éric Mercier, Valérie Boucher, Jean David, Carlos El Khoury, Karim Tazarourte

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversité Laval
FundersHospices Civils de Lyon
KeywordsMedicineEmergency medicineRetrospective cohort studyInjury Severity ScoreLogistic regressionTriageIncidence (geometry)CohortMajor traumaRevised Trauma ScoreCohort studyEmergency departmentPoison controlInternal medicineInjury preventionSurgery

Abstract

fetched live from OpenAlex

Abstract Background Direct transport of patients suffering major trauma to level-I trauma centres may reduce mortality. Emergency medical services therefore aim to limit undertriage so that all severely injured patients receive proper vital trauma care. Nevertheless, undertriage have been poorly examined in a physician-led prehospital system. The main objective of this study was to assess the incidence of undertriage. We also sought to determine its potential risk factors, as well as to assess its association with mortality. Methods A multicentre retrospective cohort study was performed using 2011–2017 data from a French regional trauma registry (RESUVal) that includes prehospital, and in-hospital data on trauma patients. All adults assessed by a physician-led mobile medical team with major trauma (Injury Severity Score [ISS] ≥ 16) were included. Major trauma patients transported directly to a level-I trauma centre were considered as correctly triaged. Multivariate logistic regression was used to identify factors associated with undertriage. Results 7,110 trauma patients were screened, of whom 2,591 had an ISS ≥ 16. Median age was 42 (IQR 27–59) years old, 75.0% were male and 12.4% (n = 320) were undertriaged. In-hospital mortality was 18.3% among undertriaged patients vs 16.2% among correct-triaged patients (p = 0.473). Patients aged 51–65 years had higher risk for undertriage (OR = 1.60, 95%CI [1.11;2.26], p = 0.01). Conversely, mechanism (fall from height 0.62 [0.45;0.86], p = 0.01; gunshot/stab wounds 0.45 [0.22;0.90], p = 0.02), longer on-scene time (> 60 minutes, 0.62 [0.40;0.95], p = 0.03), prehospital endotracheal intubation (0.53 [0.39;0.71], p < 0.001), and prehospital focused assessment with sonography FAST (0.15 [0.08;0.29], p < 0.001) were associated with a lower risk for undertriage. After adjusting on severity, undertriage was not significantly associated with a greater risk of mortality (1.22 [0.80;1.89], p = 0.36). Conclusions In our region-wide, physician-led prehospital EMS system, undertriage in major trauma was higher than recommended and advanced age was associated with higher risk for undertriage. Conversely, a pre-hospital FAST was associated with a lower risk for undertriage. Specific triage procedures should be discussed in older trauma patients and further studies are needed to evaluate the impact of prehospital FAST on triage performance. We noted that undertriaged patients had no higher risk for mortality suggesting no impact of secondarily transfer and/or high trauma care quality in level-II trauma centres. Undertriage definition should be tailored to fit local trauma systems organization.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.369
Teacher spread0.331 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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