Willingness of patients to change surgeons for a shorter waiting time for joint arthroplasty
Bibliographic record
Abstract
BACKGROUND: To improve access to care, many jurisdictions have proposed waiting-time benchmarks and guarantees. We assessed the willingness of patients to consider changing their surgeon to one with a shorter waiting time for arthroplasty. METHODS: We mailed a questionnaire to 2 random samples of patients who either were awaiting hip or knee replacement arthroplasty or had had one of these procedures within the preceding 3-12 months. We used logistic regression to assess the determinants of patients' likelihood to consider changing surgeons. RESULTS: Of 1200 responses from a sample of 2000, 557 (46%) were from patients awaiting surgery and 643 (54%) were from people who had undergone surgery. The mean age of respondents was 69.9 years (standard deviation 10.8), and 682 (57%) were women. The median waiting time for surgery was 8 months. Overall, 753 (63%) of the patients were unlikely to consider changing surgeons. Increased likelihood of changing surgeons was associated with male sex (adjusted odds ratio [OR] 1.49, 95% confidence interval [CI] 1.10-2.02), a high school education or higher (OR 1.73, 95% CI 1.15-2.62) and having already undergone surgery (OR 1.71, 95% CI 1.19-2.46). Decreased likelihood was associated with preference for a particular surgeon before referral (OR 0.57, 95% CI 0.42-0.79), a better score on the EuroQol (EQ-5D) index (a measure of health-related quality of life) (OR 0.39, 95% CI 0.24-0.66), perception that the waiting time to see the surgeon was acceptable (OR 0.50, 95% CI 0.36-0.70), perception that the waiting time to surgery was acceptable (OR 0.62, 95% CI 0.43-0.91) and perceived fairness of treatment (OR 0.53, 95% CI 0.36-0.78). INTERPRETATION: Despite long waits for surgery, most patients, if given the choice, would be unlikely to change their surgeon to one with a shorter waiting time.
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 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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".