Absence from work in the 12 months following mild traumatic brain injury in Europe: a CENTER-TBI cohort study
Bibliographic record
Abstract
BACKGROUND: Most of the prior research on absence from work after a mild traumatic brain injury (mTBI) was of a small sample size and had a limited number of follow-up assessments. OBJECTIVES: Therefore, this study investigated the prevalence of absence from work, trajectories, and associated factors in the 12 months following mTBI in Europe. METHODS: Data from a European cohort (CENTER-TBI) were used. Absence from work was assessed at 2 weeks, 3 months, 6 months, and 12 months after mTBI. Associated factors included sociodemographic factors, current psychoactive substance use, pre-injury medical history, injury-related factors, medical care, complications, and discharge, and 2-week follow-up questionnaires. Inferential analyses relied on generalized estimating equations. RESULTS: This study included 1080 adults with mTBI who were working at the time of the injury (median [IQR] age, 46.0 [23.0] years; 69 % men). Absence from work decreased from 32 % at 2 weeks to 20 % at 12 months after the injury (P < 0.001). Around 76 % of adults returned to work within the first 3 months, whereas > 43 % of those absent from work at 3 months remained absent at 12 months. The 3 factors with the strongest association with absence from work were admission to hospital wards (OR = 2.57) or intensive care units (OR = 4.76), the presence of a pre-injury psychiatric disorder (OR = 2.55), and older age (OR = 1.61). CONCLUSIONS: One-fifth of workers with mTBI were absent from work 12 months after the injury. Early identification of those at particular risk for not returning to work should be a clinical priority. STUDY REGISTRATION: NCT02210221 (https://clinicaltrials.gov/).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".