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Record W4387874327 · doi:10.3390/traumacare3040022

Impact of Sex on Completion of Life-Saving Interventions for Severely Injured Patients: A Retrospective Cohort Study

2023· article· en· W4387874327 on OpenAlexafffund
Doriane Deloye, Alexandra Nadeau, Amanda Barnes-Métras, Christian Malo, Marcel Émond, Lynne Moore, Pier‐Alexandre Tardif, Axel Benhamed, Xavier Dubucs, Pierre-Gilles Blanchard, Éric Mercier

Bibliographic record

VenueTrauma Care · 2023
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversité Laval
FundersFondation CHU de Québec
KeywordsMedicineRetrospective cohort studyEmergency departmentPsychological interventionEmergency medicineNeurosurgeryIntubationCohortSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Sex disparities in access and quality of care are well known for some time-sensitive conditions. However, the impact of sex on early trauma care remains unknown. In this study, we compared delays of completion of life-saving interventions (LSIs) between females and males among severely injured patients. This is a retrospective cohort study of all patients who consulted or were transported by ambulance in the emergency department (ED) of a level-one trauma centre following injury between September 2017 and December 2019 and for whom at least one LSI was performed. The list of LSIs was established by an expert consensus and included trauma team leader (TTL) activation, endotracheal intubation, chest decompression, blood transfusion, massive transfusion protocol, neurosurgery, spinal surgery, intestinal surgery, and spleen, liver and/or kidney angiography. A total of 905 patients were included. No significant statistical differences in the LSI delays were found when comparing females and males brought directly to the ED and transferred from another health care setting. Results of this study suggest that delays before completion of LSIs are similar for severely injured patients at our major trauma centre regardless of their sex.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.378
Teacher spread0.322 · 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 teacher head, 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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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