Are patients willing to pay for total shoulder arthroplasty? Evidence from a discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Total shoulder arthroplasty (TSA) is a common treatment to decrease pain and improve shoulder function in patients with severe osteoarthritis (OA). In Canada, patients requiring this procedure often wait a year or more. Our objective was to determine patient preferences related to accessing TSA, specifically comparing out-of-pocket payments for treatment, travel time to hospital, the surgeon's level of experience and wait times. METHODS: We administered a discrete choice experiment among patients with endstage shoulder OA currently waiting for TSA. Respondents were presented with 14 different choice sets, each with 3 options, and they were asked to choose their preferred scenario. A conditional logit regression model was used to estimate the relative preference and willingness to pay for each attribute. RESULTS: Sixty-two respondents completed the questionnaire. Three of the 4 attributes significantly influenced treatment preferences. Respondents had a strong preference for an experienced surgeon (mean 0.89 ± standard error [SE] 0.11), while reductions in travel time (-0.07 ± 0.04) or wait time (-0.04 ± 0.01) were of less importance. Respondents were found to be strongly averse (-1.44 ± 0.18) to surgical treatment by a less experienced surgeon and to paying out-of-pocket for their surgical treatment (-0.56 ± 0.05). CONCLUSION: Our results suggest that patients waiting for TSA to treat severe shoulder OA have minimal willingness to pay for a reduction in wait time or travel time for surgery, yet will pay higher amounts for treatment by an experienced surgeon.
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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 | 0.037 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".