Discordance in TKA Expectations Between Patients and Surgeons
Bibliographic record
Abstract
BACKGROUND: Aligning patient and surgeon expectations preoperatively may lead to better postoperative medical and rehabilitation compliance and therefore improve outcomes and increase satisfaction. QUESTIONS/PURPOSES: We (1) determined the rate of discordantly high patient expectations compared with those of their surgeon in patients undergoing TKA; and (2) evaluated the impact of the preoperative educational class, patient characteristics, and functional status on the likelihood of having discordantly high patient expectations. METHODS: We enrolled 205 patients awaiting TKA. Each patient completed a validated questionnaire that addresses expectations of postoperative pain relief, function, and well-being as part of a preoperative assessment. The surgeon completed the same expectations questionnaire preoperatively blinded to their patient's response. Patients had discordantly high expectations if their scores were ≥ 7 points higher than the surgeon on a 0 to 100 score range. Regression analysis was performed to determine the effect of class, patient characteristics, and functional status on the likelihood of having discordantly high patient expectations. RESULTS: Thirty-seven percent of the patients had expectation scores ≥ 7 points higher than those of their surgeon. Patients were less likely to have discordantly higher expectations if they were female (OR, 0.56; CI, 0.32-0.97) and if their pain level was high (OR, 0.99; CI, 0.98-0.99). Patients were more likely to have discordantly higher expectations if they filled out the expectations survey before rather than after the preoperative educational class (OR, 1.80; CI, 1.08-3.01). CONCLUSIONS: With increasing TKA use, surgeons will likely encounter more patients with discordantly high expectations. The preoperative educational class can be used to target patients more likely to have discordantly high expectations.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".