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Record W2793763621 · doi:10.1007/s00167-018-4879-5

The WOMAC score can be reliably used to classify patient satisfaction after total knee arthroplasty

2018· article· en· W2793763621 on OpenAlexaboutno aff
Lucy Walker, Michelle Bardgett, David J. Weir, Jim Holland, Craig Gerrand, David J. Deehan

Bibliographic record

VenueKnee Surgery Sports Traumatology Arthroscopy · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACMedicineOsteoarthritisPatient satisfactionPhysical therapyReceiver operating characteristicCohortRetrospective cohort studyDemographicsSurgeryInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

PURPOSE: The primary aim of this study was to define a classification in the WOMAC score after total knee arthroplasty (TKA) according to patient satisfaction. The secondary aims were to describe patient demographics for each level of satisfaction. METHODS: A retrospective cohort consisting of 2589 patients undergoing a primary TKA were identified from an established arthroplasty database. Patient demographics, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and short form (SF) 12 scores were collected pre-operatively and 1 year post-operatively. In addition, patient satisfaction was assessed at 1 year with four responses: very satisfied, satisfied, dissatisfied or very dissatisfied. Receiver operating characteristic (ROC) curves were used to identify values in the components and total WOMAC scores that were predictive of each level of satisfaction, which were used to define the categories of excellent, good, fair and poor. RESULTS: At 1 year, there were 1740 (67.5%) very satisfied, 572 (22.2%) satisfied, 190 (7.4%) dissatisfied and 76 (2.9%) very dissatisfied patients. ROC curve analysis identified excellent, good, fair and poor categories for the pain (> 78, 59-78, 44-58, < 44), function (> 72, 54-72, 41-53, < 41), stiffness (> 69, 56-69, 43-55, < 43) and total (> 75, 56-75, 43-55, < 43) WOMAC scores, respectively. Patients with lung disease, diabetes, gastric ulcer, kidney disease, liver disease, depression, back pain, with worse pre-operative functional scores (WOMAC and SF-12) and those with less of an improvement in the scores, had a significantly lower level of satisfaction. CONCLUSION: This study has defined a post-operative classification of excellent, good, fair and poor for the components and total WOMAC scores after TKA. The predictors of level of satisfaction should be recognised in clinical practice and patients at risk of a lower level of satisfaction should be made aware in the pre-operative consent process. LEVEL OF EVIDENCE: III.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.257
Teacher spread0.240 · 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

Citations71
Published2018
Admission routes1
Has abstractyes

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