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International Use of the AMA Guides® to the Evaluation of Permanent Impairment

2020· article· en· W3153656734 on OpenAlexaboutno aff
Mohammed I. Ranavaya, Christopher R. Brigham

Bibliographic record

VenueAMA guides newsletter · 2020
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.104
GPT teacher head0.338
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes1
Has abstractyes

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