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The rate and predictors of patient satisfaction after total knee arthroplasty are influenced by the focus of the question

2018· article· en· W2807870646 on OpenAlexaboutno aff
Nick D. Clement, Michelle Bardgett, David J. Weir, James Holland, Craig Gerrand, David J. Deehan

Bibliographic record

VenueThe Bone & Joint Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWOMACConfoundingPatient satisfactionLogistic regressionPhysical therapyOsteoarthritisOdds ratioArthroplastyCohortRetrospective cohort studyInternal medicineSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

Aims: The primary aim of this study was to assess the rate of patient satisfaction one year after total knee arthroplasty (TKA) according to the focus of the question asked. The secondary aims were to identify independent predictors of patient satisfaction according to the focus of the question. Patients and Methods: A retrospective cohort of 2521 patients undergoing a primary unilateral TKA were identified from an established regional arthroplasty database. Patient demographics, comorbidities, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and 12-Item Short-Form Health Survey (SF-12) scores were collected preoperatively and one year postoperatively. Patient satisfaction was assessed using four questions, which focused on overall outcome, activity, work, and pain. Logistic regression analysis was used to identify independent preoperative predictors of increased stiffness when adjusting for confounding variables. Results: Using patient satisfaction with the overall outcome (n = 2265, 89.8%) as the standard, there was no difference in the rate of satisfaction for pain relief (n = 2277, odds ratio (OR) 0.95, 95% confident intervals (CI) 0.79 to 1.14, p = 0.60), but patients were more likely to be dissatisfied with activities (79.3%, n = 2000/2521, OR 2.22, 95% CI 1.96 to 2.70, p < 0.001) and work (85.8%, n = 2163/2521, OR 1.47, 95% CI 1.23 to 1.75, p < 0.001). Logistic regression analysis identified different predictors of satisfaction for each of the focused satisfaction questions. Overall satisfaction was influenced by diabetes (p = 0.03), depression (p = 0.004), back pain (p < 0.001), and SF-12 physical (p = 0.008) and mental (p = 0.01) components. Satisfaction with activities was influenced by depression (p = 0.001), back pain (p < 0.001), WOMAC stiffness score (p = 0.03), and SF-12 physical (p < 0.001) and mental (p < 0.001) components. Satisfaction with work was influenced by depression (p = 0.007), back pain (p < 0.001), WOMAC function (p = 0.04) and stiffness (p = 0.05) scores, and SF-12 physical (p < 0.001) and mental (p < 0.001) components. Satisfaction with pain relief was influenced by diabetes (p < 0.001), back pain (p < 0.001), and SF-12 mental component (p = 0.04). Conclusion: The focus of the satisfaction question significantly influences the rate and the predictors of patient satisfaction after TKA. Cite this article: Bone Joint J 2018;100-B:740-8.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.223
Teacher spread0.217 · 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.

Study designObservational
DomainMethods
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

Citations50
Published2018
Admission routes1
Has abstractyes

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