Doubly Robust Estimator for Indirectly Standardized Mortality Ratios
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Routinely collected administrative and clinical data are increasingly being utilized for comparing quality of care outcomes between hospitals. This problem can be considered in a causal inference framework, as such comparisons have to be adjusted for hospital-specific patient case-mix, which can be done using either an outcome or assignment model. It is often of interest to compare the performance of hospitals against the average level of care in the health care system, using indirectly standardized mortality ratios, calculated as a ratio of observed to expected quality outcome. A doubly robust estimator makes use of both outcome and assignment models in the case-mix adjustment, requiring only one of these to be correctly specified for valid inferences. Doubly robust estimators have been proposed for direct standardization in the quality comparison context, and for standardized risk differences and ratios in the exposed population, but as far as we know, not for indirect standardization. We present the causal estimand in indirect standardization in terms of potential outcome variables, propose a doubly robust estimator for this, and study its properties. We also consider the use of a modified assignment model in the presence of small hospitals.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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