Vocational Outcomes After Traumatic Brain Injury; Prevalence and Risk Factors After 1 Year in a Multivariable Model
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of employment status (ES) or full-time study after traumatic brain injury (TBI) in a representative population and its predictive factors. DESIGN: Prospective cohort study. SETTING: Regional Major Trauma Centre. Participants: In total, 1734 consecutive individuals of working age, admitted with TBI to a Regional Trauma Centre, were recruited and followed up at 8 weeks and 1 year with face-to-face interview. Median age was 37.2 years (17.5-58.2); 51% had mild TBI, and 36.8% had a normal computed tomographic (CT) scan. MAIN OUTCOME MEASURE: Complete or partial/modified return to employment or study as an ordinal variable. RESULTS: At 1 year, only 44.9% returned to full-time work/study status, 28.7% had a partial or modified return, and 26.4% had no return at all. In comparison with status at 6 weeks, 9.9% had lower or reduced work status. Lower ES was associated with greater injury severity, more CT scan abnormality, older age, mechanism of assault, and presence of depression, alcohol intoxication, or a psychiatric history. The multivariable model was highly significant (P < .001) and had a Nagelkerke R2 of 0.353 (35.3%). CONCLUSIONS: Employment at 1 year is poor and changes in work status are frequent, occurring in both directions. While associations with certain features may allow targeting of vulnerable individuals in future, the majority of model variance remains unexplained and requires further investigation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".