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Record W2161096639 · doi:10.1002/ajim.22418

Epidemiology of work‐related traumatic brain injury: A systematic review

2015· review· en· W2161096639 on OpenAlexafffund
Vicky C. Chang, E. Niki Guerriero, Angela Colantonio

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2015
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstitutePublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPsycINFOCINAHLMedicineEpidemiologyMEDLINEOccupational safety and healthInjury preventionPoison controlOccupational medicineHuman factors and ergonomicsSuicide preventionEnvironmental healthFamily medicineGerontologyPsychiatryPsychological interventionPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: This systematic review aimed to describe the burden and risk factors of work-related traumatic brain injury (wrTBI) and evaluate methodological quality of existing literature on wrTBI. METHODS: A search of electronic databases (MEDLINE, EMBASE, PsycINFO, and CINAHL) was conducted to identify articles published between 1980 and 2013 using a combination of terms for work, TBI, and epidemiology, without geographical limitations. RESULTS: Ninety-eight studies were included in this review, of which 24 specifically focused on wrTBI. In general, male workers, those in the youngest and oldest age groups, and those working in the primary (e.g., agriculture, forestry, mining) or construction industries were more likely to sustain wrTBI, with falls being the most common mechanism of injury. CONCLUSIONS: This review identified workers at highest risk of wrTBI, with implications for prevention efforts. Future research of better methodological quality is needed to provide a more complete picture of the epidemiology of wrTBI.

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)0.007
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0140.016
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
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 head0.403
GPT teacher head0.497
Teacher spread0.094 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations68
Published2015
Admission routes2
Has abstractyes

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