International Use of the AMA Guides to the Evaluation of Permanent Impairment
Bibliographic record
Abstract
Abstract In the United States, the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) is used in state and federal workers’ compensation systems and in automobile casualty and personal injury arenas. The AMA Guides is used in similar ways internationally. Most workers’ compensation jurisdictions in Canada use the AMA Guides formally by statute or regulation or accept its use informally as a standard tool to rate impairment. In Australia, the AMA Guides is used in both federal (Australian Commonwealth) and individual states’ (or territories’) compensation schemes; two tables show how almost all states in Australia have legislated various editions of the AMA Guides for use in workers’ compensation and motor traffic accident compensation schemes. New Zealand's Accident Compensation Commission (ACC) previously used the AMA Guides, Fourth Edition; beginning in July 2011 ACC uses the sixth edition. Hong Kong uses the AMA Guides as a reference in evaluating workers’ compensation and motor vehicle claims; Malaysia uses the AMA Guides officially in adjudication; and impairment rating in Asian countries such as Taiwan, Korea, and Singapore are influenced by the philosophy and principles of the AMA Guides. South Africa uses the AMA Guides, Sixth Edition, to determine serious injury, and other editions are used in South Africa's workers’ compensation schemes. Many countries in Europe and the Middle East use the AMA Guides as a reference for determining impairment and in workers’ compensation and social welfare schemes.
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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 | 0.034 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".