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Record W2149889797 · doi:10.1186/1471-2474-12-149

Decision quality instrument for treatment of hip and knee osteoarthritis: a psychometric evaluation

2011· article· en· W2149889797 on OpenAlexaff
Karen Sepucha, Dawn Stacey, Catharine Clay, Yuchiao Chang, Carol Cosenza, Geoffrey F. Dervin, Janet M. Dorrwachter, Sandra Feibelmann, Jeffrey N. Katz, Stephen Kearing, Henrik Malchau, Monica Taljaard, Ivan M. Tomek, Peter Tugwell, Carrie A. Levin

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthMassachusetts General Hospital
KeywordsConcordanceMedicinePhysical therapyOsteoarthritisQuality of life (healthcare)Logistic regressionSports medicineReliability (semiconductor)Internal medicineAlternative medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: A high quality decision requires that patients who meet clinical criteria for surgery are informed about the options (including non-surgical alternatives) and receive treatments that match their goals. The aim of this study was to evaluate the psychometric properties and clinical sensibility of a patient self report instrument, to measure the quality of decisions about total joint replacement for knee or hip osteoarthritis. METHODS: The performance of the Hip/Knee Osteoarthritis Decision Quality Instrument (HK-DQI) was evaluated in two samples: (1) a cross-sectional mail survey with 489 patients and 77 providers (study 1); and (2) a randomized controlled trial of a patient decision aid with 138 osteoarthritis patients considering total joint replacement (study 2). The HK-DQI results in two scores. Knowledge items are summed to create a total knowledge score, and a set of goals and concerns are used in a logistic regression model to develop a concordance score. The concordance score measures the proportion of patients whose treatment matched their goals. Hypotheses related to acceptability, feasibility, reliability and validity of the knowledge and concordance scores were examined. RESULTS: In study 1, the HK-DQI was completed by 382 patients (79%) and 45 providers (58%), and in study 2 by 127 patients (92%), with low rates of missing data. The DQI-knowledge score was reproducible (ICC = 0.81) and demonstrated discriminant validity (68% decision aid vs. 54% control, and 78% providers vs. 61% patients) and content validity. The concordance score demonstrated predictive validity, as patients whose treatments were concordant with their goals had more confidence and less regret with their decision compared to those who did not. CONCLUSIONS: The HK-DQI is feasible and acceptable to patients. It can be used to assess whether patients with osteoarthritis are making informed decisions about surgery that are concordant with their goals.

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)0.020
metaresearch head score (Gemma)0.038
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 score0.020
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.291
GPT teacher head0.458
Teacher spread0.167 · 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

Citations97
Published2011
Admission routes1
Has abstractyes

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