Bibliographic record
Abstract
Background There is a need tomore 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 neckpain as an example, nature of injury andpart of body codeswere 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. Am. J. Ind. Med. 49: 557–568, 2006.2006Wiley-Liss, Inc. KEY WORDS: measurement; diagnosis; bias; neck injury; workers ’ compensation; occupational injury; administrative database
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.898 | 0.828 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".