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Record W1972554618 · doi:10.1155/2014/263241

Outcome in Women with Traumatic Brain Injury Admitted to a Level 1 Trauma Center

2014· article· en· W1972554618 on OpenAlexaffabout
Élaine de Guise, Joanne LeBlanc, Jehane H. Dagher, Simon Tinawi, Julie Lamoureux, Judith Marcoux, Mohammed Maleki, Mitra Feyz

Bibliographic record

VenueInternational Scholarly Research Notices · 2014
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsTraumatic brain injuryGlasgow Outcome ScaleTrauma centerConfoundingMedicineAlgorithmInjury preventionPoison controlInternal medicineEmergency medicineRetrospective cohort studyPsychiatryMathematics

Abstract

fetched live from OpenAlex

Background. The aim of this study was to compare acute outcome between men and women after sustaining a traumatic brain injury (TBI). Methods. A total of 5,642 patients admitted to the Traumatic Brain Injury Program of the McGill University Health Centre-Montreal General Hospital between 2000 and 2011 and diagnosed with a TBI were included in the study. The overall percentage of women with TBI was 30.6% (n = 1728). Outcome measures included the length of stay (LOS), the Extended Glasgow Outcome Scale (GOSE), the functional independence measure instrument (FIM), discharge destination, and mortality rate. Results. LOS, GOSE, the FIM ratings, and discharge destination did not show significant differences between genders once controlling for several confounding variables and running the appropriate diagnostic tests (P < 0.05). However, women had less chance of dying during their acute care hospitalization than men of the same age, with the same TBI severity and following the same mechanism of injury. Although gender was a statistically significant predictor, its contribution in explaining variation in mortality was small. Conclusion. More research is needed to better understand gender differences in mortality; as to date, the research findings remain inconclusive.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.121
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.185
GPT teacher head0.447
Teacher spread0.262 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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