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Record W3015941420 · doi:10.1016/j.jphys.2020.02.006

Appraisal of Clinical Practice Guideline: Clinical Practice Guidelines for Pain Management in Acute Musculoskeletal Injury

2020· article· en· W3015941420 on OpenAlexaff
Armaghan Dabbagh, Joy C. MacDermid

Bibliographic record

VenueJournal of physiotherapy · 2020
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineGuidelineMusculoskeletal painClinical PracticePhysical therapyMusculoskeletal injuryPain managementIntensive care medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

© 2020 Australian Physiotherapy Association. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) This clinical appraisal was published as: Dabbagh, A. & MacDermid, J. C. (2020). Appraisal of Clinical Practice Guideline: Clinical Practice Guidelines for Pain Management in Acute Musculoskeletal Injury. Journal of Physiotherapy, 66(2), 134. DOI: https://doi.org/10.1016/j.jphys.2020.02.006

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)0.098
metaresearch head score (Gemma)0.361
Version: metacan-v3-hybrid-931329e0061cValidation 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: Review · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.519

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.361
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0100.008
Science and technology studies0.0050.003
Scholarly communication0.0100.006
Open science0.0070.008
Research integrity0.0210.016
Insufficient payload (model declined to judge)0.0080.006

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 head0.235
GPT teacher head0.638
Teacher spread0.403 · 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 designNot applicable
Domainnot available
GenreReview

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".

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Citations4
Published2020
Admission routes1
Has abstractno

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