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State-by-State Use of AMA Guides

2019· article· en· W4251708945 on OpenAlexaboutno aff

Bibliographic record

VenueAMA guides newsletter · 2019
Typearticle
Languageen
FieldMedicine
TopicGynecological conditions and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)Computer scienceProgramming language

Abstract

fetched live from OpenAlex

Abstract This article presents a history of the origins and development of the AMA 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, the AMA Guides has 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 the AMA Guides for 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 the AMA Guides used in each state and the enabling statute/code, with comments.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.001

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 head0.025
GPT teacher head0.266
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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