Offering patients the opportunity to choose their hospital for total knee replacement: Impact on satisfaction with the surgery
Bibliographic record
Abstract
OBJECTIVE: To describe the extent to which patients were offered a choice between 2 or more hospitals for total knee replacement (TKR); to examine the association between having a choice of hospital for TKR and satisfaction with the surgery; and to identify population groups less likely to be offered a choice. METHODS: We studied a population-based sample of 932 Medicare beneficiaries who underwent elective TKR in 2000. We surveyed patients about their participation in choosing a hospital and their satisfaction with surgery. We examined whether lack of hospital choice influenced satisfaction with surgery after adjusting for age, sex, preoperative function, and socioeconomic status. RESULTS: Among 932 TKR recipients (mean age 74 years, 67% women), more than half (53%) reported having a lack of hospital choice. After adjusting for socioeconomic status, patients reporting lack of choice were approximately twice as likely to be dissatisfied with the results of surgery as patients who reported choosing among 2 or more hospitals for TKR (odds ratio [OR] 2.09, 95% confidence interval [95% CI] 1.13-3.87). Results of logistic regression revealed that patients reporting lack of choice were more likely to be women (OR 1.52, 95% CI 1.14-2.04), >80 years of age (as compared with 65-70 years; OR 1.63, 95% CI 1.03-2.57), living in suburban areas (OR 1.68, 95% CI 1.23-2.30), nonwhite (OR 1.57, 95% CI 0.86-2.87), and were less likely to have TKR performed by a high-volume surgeon (OR 0.71, 95% CI 0.53-0.96). CONCLUSION: More than half of the patients did not have a choice in selecting the hospital where they had TKR. Patients reporting lack of choice were more likely to be dissatisfied with surgery. Interventions to address preferences for hospital may improve satisfaction with care for patients with advanced knee arthritis.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".