‘There are too many of us to fix.’ Patients’ views of acceptable waiting times for hip and knee replacement
Bibliographic record
Abstract
OBJECTIVES: To assess patients' views of maximum acceptable waiting times (MAWT) for hip and knee replacement, associated factors and the accuracy of self-reported waiting times. METHODS: We mailed 1000 questionnaires each to two random samples of patients either waiting for or who had received an arthroplasty within the preceding 3-12 months. We used linear regression to assess the determinants of patient MAWT, and content analysis to assess reasons for MAWT and ideal waiting time. RESULTS: Of the 1330 responses, 1127 had MAWT data. The sample was 57% women; mean age was 70 +/- 11 years. Median self-reported and actual waiting time was eight months (Spearman correlation = 0.70). Median MAWT was four months and ideal waiting time was two months. The most frequent reasons for MAWT were pain, quality of life and needing time to prepare for surgery. A longer MAWT was associated with younger age, group (waiting), a longer self-reported waiting time, better EQ-5D index, an acceptable waiting time, a perception of fairness and a view that others worse off on the list should go ahead. CONCLUSIONS: Patients' views of acceptable waiting times are important for a fair process of establishing waiting time benchmarks for joint replacement.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".