International Use of the AMA Guides® to the Evaluation of Permanent Impairment
Bibliographic record
Abstract
Abstract Since its inception more than six decades ago, the AMA Guides to the Evaluation of Permanent Impairment, (AMA Guides), has become internationally accepted as a global benchmark and is used in the United States, Canada, certain European countries, the Middle East, Australia, New Zealand, and Southern Africa, as well as by the United Nations. When the AMA Guides, Sixth Edition, adopted the terminology and conceptual framework of disablement developed by the World Health Organization, this paradigm shift let to an increase in the worldwide influence and use of the AMA Guides. In the United States, the AMA Guides is used primarily in state and federal workers’ compensation systems and sometimes in automobile casualty and personal injury arenas. Most workers’ compensation jurisdictions across Canada use the AMA Guides formally by statute or regulation, or they accept its use informally as a standard tool to rate impairment. In Australia, the AMA Guides is used in both federal and individual state or territory compensation schemes for personal injuries that arise from work, as well as motor vehicle accidents (a table presents uses of the AMA Guides in Australian jurisdictions). New Zealand uses the AMA Guides, Fourth Edition, and the ACC User Handbook to the AMA “Guides to the Evaluation of Permanent Impairment,” Fourth Edition. The AMA Guides is used in Hong Kong to evaluate all types of damages for personal injury claims and also is referenced in Southern Africa, Europe, and countries in the Middle East.
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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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.026 |
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".