Return to work following road accidents: Factors associated with late work resumption
Bibliographic record
Abstract
OBJECTIVE: To analyse factors associated with late return to work in road accident victims. MATERIALS AND METHODS: The ESPARR cohort comprises road accident victims monitored over time from initiation of hospital care. A total of 608 ESPARR cohort subjects were working at the time of their accident and answered questionnaires at 6 months and/or 1 year. For each level of overall severity of injury (Maximum - Abbreviated Injury Scale (M-AIS) 1, 2, 3 and 4-5), a time-off-work threshold was defined, beyond which the subject was deemed to be a late returner; 179 subjects were considered to be late in returning to work, while 402 showed a normal pattern of return. Logistic regression identified factors associated with late return. RESULTS: Type of journey, overall injury severity and intention to press charges emerged as factors predictive of late return to work on the basis of the data collected at inclusion alone. After adjustment, pain (odds ratio (OR): 2.6; 95% confidence interval (95% CI) 1.0-6.7) and physical sequelae (OR: 3.8; 95% CI 1.7-8.3) at 6 months and the fact of pressing charges (OR: 2.6; 95% CI 1.2-5.5) remained significantly linked with late return to work. CONCLUSION: Impaired health status at 6 months after the initial accident (in the form of persistent pain and physical sequelae) is a determining factor delaying return to work following a road traffic accident.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".