State-by-State Use of AMA Guides
Bibliographic record
Abstract
Abstract This article presents a history of the origins and development of theAMA Guides to the Evaluation of Permanent Impairment(AMA Guides), from the publication of an article titled “A Guide to the Evaluation of Permanent Impairment of the Extremities and Back” (1958) until a compendium of thirteen guides was published in book form in 1971. The most recent, sixth edition, appeared in 2008. Over time, theAMA Guideshas been widely used by US states for workers’ compensation and also by the Federal Employees Compensation Act, the Longshore and Harbor Workers’ Compensation Act, as well as by Canadian provinces and other jurisdictions around the world. In the United States, almost twenty states have developed some form of their own impairment rating system, but some have a narrow range and scope and advise evaluators to consult theAMA Guidesfor a final determination of permanent disability. An evaluator's impairment evaluation report should clearly document the rater's review of prior medical and treatment records, clinical evaluation, analysis of the findings, and a discussion of how the final impairment rating was calculated. The resulting report is the rating physician's expert testimony to help adjudicate the claim. A table shows the edition of theAMA Guidesused in each state and the enabling statute/code, with comments.
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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 | 0.032 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.039 |
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".