MétaCan
Menu
Back to cohort
Record W3081863687 · doi:10.31128/ajgp-08-19-5051-03

National osteoarthritis strategy brief report: Advanced care

2020· article· en· W3081863687 on OpenAlexaff
Xia Wang, David J. Hunter, Michelle M. Dowsey, Ian A. Harris, Peter O’Sullivan, Bill Donnelly, Thomas Buttel, Anita E. Wluka, P. Clark, Raj Anand, Yuen Leow, Jane Gunn, Yingyu Feng, Peter Choong

Bibliographic record

VenueAustralian Journal of General Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsInstitute of Aging
FundersNational Health and Medical Research CouncilMedibank Better Health FoundationMedical Research CouncilAustralian Orthopaedic AssociationArthritis Australia
KeywordsOsteoarthritisMedicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Osteoarthritis (OA) is one of the most common chronic joint diseases and a leading cause of pain and disability in Australia. A National Osteoarthritis Strategy (the Strategy) was developed to outline a national plan to achieve optimal health outcomes for people at risk of, or with, OA. OBJECTIVE: This article focuses on the theme of advanced care of patients with OA within the Strategy. DISCUSSION: The Strategy was developed in consultation with a leadership group, thematic working groups, an implementation advisory committee, multisectoral stakeholders and the public. This Strategy identified three priorities in advanced care for osteoarthritis. In brief, these include surgical decision making, referral for evidence-informed non-surgical alternatives and surgical services. A set of goals within these priority areas and strategies was also proposed by the working group in consultation with stakeholders nationwide. Peak arthritis bodies and major healthcare professional associations currently endorse the Strategy.

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)0.017
metaresearch head score (Gemma)0.037
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: Other · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.004
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0240.009

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 head0.026
GPT teacher head0.343
Teacher spread0.316 · 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
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAustralian Journal of General PracticeSame topicMusculoskeletal Disorders and RehabilitationFrench-language works237,207