Legal Update: Catastrophic Impairment: Catastrophic Impairment: Use of the AMA Guides in Ontario
Bibliographic record
Abstract
Abstract Chapter 18, Pain, is the first attempt in the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), Fifth Edition, to define comprehensive pain evaluation and rating. Chapter 18 focuses on situations in which the pain itself is a major cause of suffering, dysfunction, or medical intervention even though an examining physician can find no demonstrable active disease or autonomic changes. These statements likely would be rejected by rating systems that require a physiologic basis for rating. Other chapters in the AMA Guides typically account for pain as part of specific impairment ratings; for example, a physician who rates cervical spine impairment must decide if a rating for pain beyond the original rating is appropriate. Many rating systems would reject such a rating for pain because of its subjective nature, but, despite these difficulties, even restrictive rating systems may require the use of Chapter 18 because, according to the AMA Guides, no other available method exists for considering impairment of certain conditions such as postherpetic neuralgias or migraines. As the AMA Guides acknowledges, Chapter 18 seems to allow impairment ratings caused by pain and suffering but at the same time to forestall actually assigning a number. One hopes for refinements in our understanding of chronic pain treatment and outcomes; in the interim, knowledgeable raters should guide nonmedical personnel toward a reasonable understanding of this chapter.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".