Inter-unit reliability for quality measure testing
Bibliographic record
Abstract
Facility-specific quality measures are commonly used to monitor dialysis facilities. To successfully develop, test and validate quality measures, a subset of facilities are often recruited for preliminary evaluations. To ensure that the facility-specific measures will achieve a desirable precision, it is often of interest to determine a minimum number of facilities that should be recruited. To achieve this, we propose a method based on the inter-unit reliability (IUR), which is commonly used to assess quality measures. Accurate estimates of the IUR are important to ensure that the quality measure will achieve a desirable precision. We first review existing methods of estimating the IUR for quality measures that are based on sample averages. We then generalize the IUR estimations to more complicated standardized measures. In particular, the confidence intervals of the IUR are calculated, with the width of this confidence interval measuring the precision of the estimate of the IUR. To assess the performance of the estimated IUR with various numbers of facilities, a simulation study is conducted. The IURs are then computed to develop and implement a quality measure that is used to guard against high ultrafiltration rates for adult dialysis patient with End-Stage Renal Disease. The estimated values are helpful to determine a minimum number of facilities that should be recruited in the measure testing process.
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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 | 0.165 | 0.487 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".