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Record W2892655711 · doi:10.1097/jom.0000000000001440

Comparative Analysis of Impairment Ratings From the 5th to 6th Editions of the AMA Guides

2018· article· en· W2892655711 on OpenAlexaff
Jason W. Busse, Marieke M. de Vaal, S.J. Ham, Behnam Sadeghirad, Loes W A H van Beers, Rachel Couban, Sun Makosso Kallyth, Rudolf W. Poolman

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsImpactMcMaster University
Fundersnot available
KeywordsInterquartile rangeMedicineConfidence intervalPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The association of American Medical Association's (AMA) Guides edition with impairment ratings is uncertain. METHODS: We used data from a consecutive sample of 249 injured workers referred for an independent evaluation 10 months before and after assessors switched from the 5th to the 6th edition of the AMA Guides. RESULTS: The median whole person impairment rating was 7.0% (interquartile range [IQR]: 4 to 14) for 131 claimants assessed with the 5th edition of the Guides, and 4.0% (IQR: 2 to 8) for 118 claimants assessed with the 6th edition (P-value for difference: 0.002). Multivariable analysis showed a 36.4% relative reduction (95% confidence interval [CI] 17.2% to 57.3%) in impairment rating with the 6th edition of the Guides versus the 5th edition. CONCLUSIONS: The 6th edition of the AMA Guides provides systematically lower impairment ratings for injured workers than the 5th edition.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.350
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2018
Admission routes1
Has abstractyes

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