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Record W3010601963 · doi:10.3233/bmr-181420

Responsiveness of the Turkish KOOS-PS and HOOS-PS in knee and hip joint arthroplasty patients

2020· article· en· W3010601963 on OpenAlexaboutno aff
Özlem Taşdelen, Ali Utkan, Kubilay Uğurcan Ceritoğlu, Funda Seher Özalp Ateş, Hatice Bodur

Bibliographic record

VenueJournal of Back and Musculoskeletal Rehabilitation · 2020
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACMedicineOsteoarthritisPhysical therapyArthroplastyTurkishKnee replacementSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Adaptation to Turkish language and validation studies of Knee Injury and Osteoarthritis Outcome Score - Physical Function Short Form (KOOS-PS) and Hip Disability and Osteoarthritis Outcome Score - Physical Function Short Form (HOOS-PS) were done previously but responsiveness to changes of these questionnaires could not be tested in these studies. OBJECTIVE: The aim of this study was to assess the responsiveness of the Turkish versions of the KOOS-PS and HOOS-PS in a patient group who underwent knee or hip joint arthroplasty operation. METHODS: Sixty-three patients who underwent total knee arthroplasties and sixteen patients who underwent total hip arthroplasties for primary osteoarthritis were included in this study. The preoperative and 3-month postoperative KOOS-PS, HOOS-PS, and Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index hip and knee scores were collected from the hospital records, and the effect sizes (ESs) and standardized response means (SRMs) were calculated. RESULTS: The ESs and SRMs, respectively, were as follows: -1.954 and -2.156 for the KOOS-PS, -1.833 and -2.464 for the HOOS-PS, -4.848 and -4.210 for the WOMAC-knee, and -3.835 and -4.625 for the WOMAC-hip. CONCLUSIONS: The Turkish versions of the KOOS-PS and HOOS-PS exhibited strong responsiveness to change in the arthroplasty patients.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.043
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0000.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 teacher head, 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

Citations3
Published2020
Admission routes1
Has abstractyes

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