Impairment Evaluation: Use of the Guides in Ontario for Defining “Catastrophic Impairment”: Challenges and Controversies
Bibliographic record
Abstract
Abstract Several Canadian provinces use the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) to adjudicate workers’ compensation claims, and the Province of Ontario uses the AMA Guides, Fourth Edition, to adjudicate motor vehicle accident personal injury claims. This article focuses on controversies that have arisen in Ontario regarding how the AMA Guides is applied and shows some of the challenges that occur in quantifying psychological impairment. In 2004, the Ontario Superior Court found in the Desbiens v. Mordini trial that the AMA Guides did not provide any direct methodology for estimating percentage impairment in this unique circumstance that involved pre-existing paraplegia and subsequent dramatic loss of residual functions. The judge found that, using the information available, a whole person impairment (WPI) score of 40% could be derived, but Ontario requires a minimum 55% WPI before an individual qualifies for catastrophic impairment benefits. In view of the individual's circumstances and a psychologist's recommendation, the judge awarded an additional 25% WPI. The Ontario model has been interpreted to allow subjective complaints (symptoms) to be included in the impairment evaluation process, but this approach eliminates any expectation of objectivity. If a judicial system aims to force impairment percentages onto a situation that in fact does not warrant such ratings, it should not do so by an inappropriate application of the AMA Guides.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".