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Record W4298144291 · doi:10.1787/6da7f06b-en

International assessment of the use and results of patient-reported outcome measures for hip and knee replacement surgery

2022· paratext· en· W4298144291 on OpenAlexaff
Candan Kendir, Katherine De Bienassis, Luke Slawomirski, Niek Klazinga, Micheline Turnau, Michael Terner, Greg Webster, Éric Bohm, Brian R. Hallstrom, Ola Rolfson, J. Mark Wilkinson, Anne Lübbeke-Wolff

Bibliographic record

VenueOECD health working papers · 2022
Typeparatext
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsCanadian Institute for Health Information
FundersOrganisation de Coopération et de Développement ÉconomiquesEuropean Commission
KeywordsKnee replacementWork (physics)MedicinePhysical therapyHip replacementPatient-reported outcomeHealth careOsteoarthritisQuality (philosophy)Quality of life (healthcare)ArthroplastyNursingSurgeryAlternative medicineEngineeringPolitical science

Abstract

fetched live from OpenAlex

Osteoarthritis impacts 7% of the global population, affecting more than 500 million people worldwide. As populations of OECD countries age, an increasing number of hip and knee replacement surgeries calls for further work on assessment of quality of care, particularly from patients’ point of view. Thirteen programmes from nine countries participated in the PaRIS Hip and Knee PROMs comparative reporting in 2020-21 by collecting and submitting data by generic and condition-specific PROMs. All programmes showed improvements in patient outcomes though the relative improvement varied. Crosswalks from SF-12 to EQ-5D provided valuable lessons on conversion errors. Results of this work call for improving the use of data for comparative reporting as well as further collaboration on utilising patient-reported metrics in quality-of-care improvement and policymaking.

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)0.056
metaresearch head score (Gemma)0.108
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: none
Teacher disagreement score0.056
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.108
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0130.025
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.102
GPT teacher head0.356
Teacher spread0.254 · 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

Citations258
Published2022
Admission routes1
Has abstractyes

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