Predicting Patient Dissatisfaction Following Joint Replacement Surgery
Bibliographic record
Abstract
OBJECTIVE: The incidence of patient-reported dissatisfaction following total joint arthroplasty can be up to 30%. Our aim was to identify the preoperative patient-level predictors of patient dissatisfaction 1 year after surgery. METHODS: We surveyed 1720 patients undergoing primary hip or knee replacement surgery. Relevant covariates including demographic data, body mass index, sex, comorbidities, and education were recorded. Joint functional status and patient quality of life were assessed at baseline and at 1-year followup with the Western Ontario McMaster University Osteoarthritis Index (WOMAC) and Medical Outcomes Study Short Form-36 (SF-36) scales, respectively. Patient satisfaction with surgery was determined with 4 survey questions at 1-year followup. RESULTS: There were no significant differences in demographic data between satisfied (n = 1290) and dissatisfied patients (n = 430). Logistic regression modeling showed that a lower preoperative SF-36 Mental Health score independently predicted patient dissatisfaction with surgery, adjusted for all relevant covariates (p < 0.05). We found no correlation between patient satisfaction and WOMAC change scores at 1-year followup (p = 0.31). CONCLUSION: Preoperative mental health is an important factor to consider when understanding patient satisfaction with surgery. Interventions to reduce psychological distress prior to surgery should be studied to determine if they may improve subjective outcomes of patients undergoing joint replacement surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".