Patient Expectation and Satisfaction as Measures of Operative Outcome in End-Stage Ankle Arthritis
Bibliographic record
Abstract
BACKGROUND: Current operative outcome scales are based on pain and function, such as the Ankle Osteoarthritis Scale (AOS). Outcomes based on patient expectation and satisfaction may be more useful. The purpose of this prospective cohort study was to evaluate associations between patient expectation, satisfaction, and outcome scores for ankle fusion and total ankle replacement (TAR). METHODS: In total, 654 ankles in 622 patients were analyzed at a mean of 61 months. Patient expectation and satisfaction with symptoms were quantified pre- and postoperatively using the Musculoskeletal Outcomes Data Evaluation and Management Scale questionnaires from the American Academy of Orthopaedic Surgeons, while function was quantified using the AOS. RESULTS: Patients undergoing ankle replacement had a higher preoperative expectation score (79; 95% confidence interval [CI], 77-81) compared with those undergoing ankle fusion (72; 95% CI, 68-75). Preoperative expectation scores correlated weakly with AOS scores (R (2) = 0.02) and with the "expectations met" score for ankle fusion (R (2) = 0.07) but not for ankle replacement (R (2) < 0.01). Satisfaction scores were similar for ankle fusion and ankle replacement at follow-up, but a greater number of ankle replacement patients showed improvement in satisfaction (84% vs 74%, P < .005). Higher satisfaction at final follow-up was associated with better expectations met and greater improvement in AOS outcome scores for both ankle fusion and ankle replacement. Expectations met and AOS scores at follow-up correlated for ankle fusion (R (2) = 0.38, P < .0001) and ankle replacement (R (2) = 0.31, P < .0001). CONCLUSIONS: Patients undergoing TAR had higher expectation scores prior to surgery than those undergoing ankle fusion. Expectations may be more likely to be met by ankle replacement compared with ankle fusion. Ankle replacement patients were more likely to report improved satisfaction scores after surgery. Preoperative expectation scores showed little correlation with preoperative AOS scores, indicating that expectation is independent of pain and function. However, postoperative expectations met and satisfaction scores were strongly associated with AOS scores at follow-up. Better preoperative patient education may change expectations and requires study. LEVEL OF EVIDENCE: Level II, prospective cohort study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".