A Bayesian approach to risk‐adjusted outcome monitoring in healthcare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinical outcomes are commonly monitored in healthcare practices to detect changes in care providers' performance. One key challenge in outcome monitoring is the need of adjustment for patient base-line risks. Various control charting methods have been developed to conduct risk-adjusted outcome monitoring, but they all rely on the availability of a large number of historical data. We propose a Bayesian approach to this type of monitoring for cases where historical data are not available. In our approach, detection of change is formulated as a model-selection problem and solved using a popular Bayesian tool for variable selection, the Bayes factor. Issues in decision-making about whether there is a change point in the observed patient outcomes are addressed, including specification of priors and computation of Bayes factors. This approach is applied to a real data set on cardiac surgeries, and its performance under different parameter scenarios is studied through simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it