Capturing cases in workers' compensation databases: The example of neck pain
Bibliographic record
Abstract
BACKGROUND: There is a need to more accurately enumerate workers with musculoskeletal injuries who make lost-time claims to workers compensation boards. The objective of this study is to develop an approach to more accurately enumerate these workers. METHODS: Lost-time claims to the Ontario Workplace Safety & Insurance Board (WSIB) were reviewed. Using neck pain as an example, nature of injury and part of body codes were identified to classify cases. Claims of a random sample of 434 claimants were reviewed. The proportion of claimants classified as having neck pain was computed. RESULTS: The proportion of claimants classified with soft-tissue injuries to the neck varied from 0.88 for codes including "neck/cervical region," 0.69 for "back region" to 0.05 for those coded as "shoulder/upper arm." CONCLUSIONS: Restricting the enumeration of injuries to specific part of body codes can lead to a gross underestimation of the magnitude of soft-tissue disorders in epidemiological studies using workers' compensation data. The proposed approach leads to more accurate enumeration.
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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 | 0.011 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".