Prevalence of lost-time claims for mild traumatic brain injury in the working population: Improving estimates using workers compensation databases
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To test the usefulness of a method to improve the measurement of prevalent mild traumatic brain injury (MTBI) among injured workers with a workers compensation claim. METHODS: Database codes were selected to identify MTBI cases in the Ontario workers compensation lost-time claims database. A random sample of 210 claims was selected, classified as MTBI or not, and used to calculate proportions with MTBI among code groups. The annual prevalence of MTBI in 1997 and 1998 was calculated by weighting the numerators with the appropriate proportions of MTBI within each code group. RESULTS: Four code groups were created: the head region, cranial region, concussion code group and the brain region. The proportion of MTBI in each group was 29%, 19%, 92% and 32%, respectively. The 1997 prevalence depended on the codes used, from 39/10,000 (95% confidence interval (CI): 35-44) for a weighted version of the 'concussion' code to 58/10,000 (95% CI: 50-65) for inclusion of all identified MTBI codes. CONCLUSIONS: Restricting the enumeration of MTBI to specific 'concussion' codes can lead to under-estimation of the prevalence of MTBI in epidemiological studies using workers compensation data. Approximately six out of every 1000 lost-time claims are associated with MTBI. Given lost-time estimates of disability under-estimate the prevalence of this mild injury, MTBI, is an important workplace injury.
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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 | 0.033 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".