What publicly available quality metrics do hip and knee arthroplasty patients care about most when selecting a hospital in Maryland: a discrete choice experiment
Bibliographic record
Abstract
OBJECTIVE: To quantify which publicly reported hospital quality metrics have the greatest impact on a patient's simulated hospital selection for hip or knee arthroplasty. DESIGN: Discrete choice experiment. SETTING: Two university-affiliated orthopaedic clinics in the greater Baltimore area, Maryland, USA. PARTICIPANTS: One hundred and twenty-eight patients who were candidates for total hip or knee arthroplasty. PRIMARY AND SECONDARY OUTCOME MEASURES: The effect and magnitude of acceptable trade-offs between publicly reported hospital quality parameters on patients' decision-making strategies using a Hierarchical Bayes model. RESULTS: (MRSA) rates (18.8%). The understandability of the discharge instructions was deemed the least important attribute with a relative importance of 6.9%. Stratification of these results by insurance status and duration of pain prior to surgery revealed that patient demographics and clinical presentation affect the decision-making paradigm. CONCLUSIONS: Publicly available information regarding hospital performance is of interest to hip and knee arthroplasty patients. Patients are willing to accept suboptimal understanding of discharge instructions, lower hospital ratings and suboptimal cleanliness in exchange for better postoperative pain management, lower MRSA rates, and lower complication rates.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".