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Record W4213325924 · doi:10.1097/jom.0000000000002512

Impact of Timing of Mental Health Interventions for Mild Traumatic Brain Injury Patients

2022· article· en· W4213325924 on OpenAlexaff
Natasha Nanwa, Vincent Wai‐Sun Wong, Aaron Thompson

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSt. Michael's HospitalWorkplace Safety & Insurance Board
Fundersnot available
KeywordsTraumatic brain injuryMental healthPsychological interventionMedicineOccupational safety and healthInjury preventionSuicide preventionPoison controlPsychiatryPsychologyMedical emergencyEmergency medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the impact of timing of mental health interventions in workers' compensation claims for mild traumatic brain injury (MTBI). METHODS: A 10-year matched retrospective cohort study of MTBI claims. Cases who started treatment within 3 months of the date of injury were hard matched to cases who started treatment more than 3 months after the date of injury. Outcomes were incremental cost difference and loss of earnings benefit duration 1 year after first intervention. RESULTS: Seventeen percent (17%) of patients received mental health interventions. The early mental health intervention group had lower mean costs (incremental difference$1580 [95% CI: $5718 to $2085]) and shorter durations of disability (off loss of earnings) (59.2% versus 46.6%, NS). Sensitivity and stratified analyses demonstrated the same trend. CONCLUSIONS: Early mental health interventions for MTBI patients may lead to reduced health care costs and shorter durations of disability.

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)0.009
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.160
GPT teacher head0.449
Teacher spread0.289 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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