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Record W2009981645 · doi:10.7205/milmed-d-10-00252

Knowledge Gained From the Brief Traumatic Brain Injury Screen—Implications for Treating Canadian Military Personnel

2011· article· en· W2009981645 on OpenAlexaffabout
Charles Nelson, Kate St. Cyr, Margaret Weiser, Shannon Gifford, Jane Gallimore, Andrew Morningstar

Bibliographic record

VenueMilitary Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMilitary personnelMilitary medicineMental healthTraumatic brain injuryMedicineOccupational safety and healthSuicide preventionInjury preventionPsychiatryPoison controlPhysical healthHuman factors and ergonomicsMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare physical and mental health outcomes of Canadian military personnel with probable mild traumatic brain injury (mTBI) to outcomes of those without and to report implications for collaboration and treatment. METHODS: One hundred forty-seven soldiers attending the Operational Stress Injury Clinic at Parkwood Hospital, London, Ontario, were screened for mTBI and completed several other measures of mental and physical health. Scores from these measures were compared across two groups (positive vs. negative screens for mTBI) using an independent samples t-test. RESULTS: Thirty-four of 147 participants screened positively for mTBI. Soldiers with probable mTBI were more likely to have poorer physical health but were less likely to engage in problem drinking than those who screened negatively for mTBI. CONCLUSIONS: In this initial study, we found that the wide range of physical and mental health difficulties experienced by Canadian military personnel with probable mTBI necessitates an interdisciplinary collaborative care model.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.172
GPT teacher head0.366
Teacher spread0.194 · 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.

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

Citations6
Published2011
Admission routes2
Has abstractyes

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