Patient Preferences between Minimum Volume Thresholds and Nationwide Healthcare Provision: the Example of Total Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to investigate, whether patients in Germany are willing to travel a longer time to a certain hospital in order to receive a better treatment (lower 90-days mortality, lower risk of revision) in elective total knee arthroplasty. In addition, we analyzed which characteristics determined patient preference. METHODS: The participants were recruited via random samples of registration offices and hospitals. All have undergone discrete choice experiments for the outcomes mortality and revision. Descriptive statistics were used to analyze the patient's preference. Logistic regression models were applied to identify characteristics that influence decision making. RESULTS: 71.7% (mortality) and 86.11% (revision) of the respondents are willing to travel a longer time in order to lower their surgical risk. The amount of people that are willing to do so is even larger in the subgroup recruited in the hospital (78.5% respectively 90.7%). CONCLUSION: The majority of the participants are willing to travel longer to lower their surgical risk for elective knee arthroplasty. It has to be considered, that the population under study might not be representative. Patient's preferences corresponds with the aim of introducing minimum volume thresholds. Future studies should focus on other indications and outcomes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".