Update on the epidemiology of work-related traumatic brain injury: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) is a public health concern that can occur in a range of contexts. Work-related TBI (wrTBI) is particularly concerning. Despite overall work-related injury claims decreasing, the proportion of claims that are wrTBI have increased, suggesting prevention and support of wrTBI requires ongoing attention. OBJECTIVES: This review aimed to provide updated information on the burden and risk factors of wrTBI among the working adult population. METHODS: Medline, Embase, PsycINFO, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using a combination of TBI, work, and epidemiology text words and medical subject headings. Two reviewers independently assessed articles for inclusion. Meta-analyses were conducted to estimate prevalence and mortality of wrTBI and a narrative synthesis was conducted to provide additional context. RESULTS: Pooled proportions meta-analyses estimate that 17.9% of TBIs were work-related and 6.3% of work-related injuries resulted in TBI, with 3.6% of wrTBI resulting in death. Populations of wrTBI were predominantly male (76.2%) and were 40.4 years of age, on average. The most commonly reported industries for wrTBI were education and training, healthcare and social assistance, construction, manufacturing, and transportation. Falls, being struck by an object or person, motor vehicle collisions, and assaults were the most commonly reported mechanisms of wrTBI. CONCLUSIONS: A better understanding of the epidemiology of wrTBI can inform prevention and management strategies. This review highlights existing gaps, including a notable lack of sex or gender stratified data, to direct future investigation. PROSPERO REGISTRATION NUMBER: CRD42020169642.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".