The challenge of determining appropriate care in the era of patient-centered care and rising health care costs
Bibliographic record
Abstract
Appropriateness of care is typically determined in the United States by evidence on efficacy and safety, combined with the judgments of experts in research and clinical practice, but without consideration of the cost of care or patient preferences. The shift in focus towards patient-centered care calls for consideration of outcomes that are important to patients, accommodation of patient preferences, and incorporation of the costs of care in patient-provider shared clinical decisions. The RAND/UCLA Appropriateness method was designed to determine rates of appropriate or inappropriate care, but the method did not include patient preferences or costs. This essay examines how methods of studying appropriateness can be made more patient-centered by describing a modification of the RAND/UCLA method by including patient outcomes, preferences, and costs.
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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 | 0.232 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".