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Record W2791780981 · doi:10.24095/hpcdp.38.3.05

At-a-glance - Traumatic brain injury management in Canada: changing patterns of care

2018· article· en· W2791780981 on OpenAlexaffvenueabout
Deepa P. Rao, Steven McFaull, Wendy Thompson, Gayatri Jayaraman

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsMedicinePopulationEmergency departmentOccupational safety and healthFamily medicinePediatricsNursingEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: With growing awareness about traumatic brain injuries (TBI), there is limited information about population level patterns of TBI care in Canada. METHODS: We examined data from the Canadian Community Health Survey (years 2004, 2009, and 2014) among all respondents ages 12 years and older. TBI management characteristics examined included access to care within 48 hours of injury, point of care, hospital admission, and follow-up. RESULTS: We observed that many Canadians sought care within 48 hours of their injury, with no changes over time. We found a significant decline in the proportion of Canadians opting to visit an emergency department (p = 0.03, all ages), and a significant increase in youth opting to visit a doctor's office (p < 0.01). CONCLUSION: TBIs are an important and growing health concern in Canada. Care for such injuries appears to have shifted towards the use of health care professionals outside the hospital environment, including primary care doctors.

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)0.004
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 score0.045
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.051
GPT teacher head0.367
Teacher spread0.316 · 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".

Quick stats

Citations26
Published2018
Admission routes3
Has abstractyes

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