Results of a Pilot Study Reviewing Dermatitis Claims Submitted to the Ontario Workplace Safety and Insurance Board
Bibliographic record
Abstract
BACKGROUND: Workers who develop occupational skin disease are often eligible for workers' compensation benefits; however, there is little known about the decision-making process for adjudicating claims submitted for work-related skin problems. OBJECTIVE: The objective of this pilot study was to test a file abstraction instrument and determine the nature of information that was available for decision-making. METHODS: Files submitted to the Ontario Workplace Safety and Insurance Board (WSIB) in 1995 for dermatitis were identified. The last 51 files were abstracted to collect information concerning demographics, physicians seen, information available in the claim file for decision making, as well as type of claim and outcome of the claim. RESULTS: Approximately 70% of the claims were "no-lost-time" and one-third of total claims were accepted for compensation. Although there was reasonable information related to the clinical status, most claims had no information that related to workplace issues such as exposures or association with work. Claims that were for lost time or were accepted had more information available. CONCLUSIONS: The pilot study has demonstrated that there is a lack of information related to workplace issues that would be important in decision-making. The study will be extended to examine the entire year's claims in order to develop a strategy to enhance the understanding of the WSIB and providers regarding the information necessary for decision-making and to determine methods to facilitate its collection.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".