Abstract 12: National Surveillance of Stroke Quality of Care and Outcomes Using Post-stratification Survey Weights on the Get With the Guidelines-stroke Patient Registry
Bibliographic record
Abstract
Background: The U.S. lacks an appropriate stroke surveillance system. This study developed and validated post-stratification weights for an existing stroke patient registry to represent the entire U.S. population across the nine U.S. Census divisions. Methods: Two statistical approaches were used to develop post-stratification weights for the Get With The Guidelines-Stroke registry by anchoring population estimates to the National Inpatient Sample to model the burden of acute ischemic stroke. Post-stratification survey weights were estimated using a raking procedure and Bayesian interpolation methods. Both strategies for developing weights were compared. Weighting methods were adjusted to limit dispersion of weights and make reasonable national estimates of patient characteristics, quality of hospital care, and clinical outcomes. Standardized differences in national population estimates were reported between the two post-stratification methods. Color treemaps were used to visualize the distribution of post-stratification weights across relevant sub-populations. Primary measures evaluated were patient and hospital characteristics, stroke severity, vital and laboratory measures, disposition, and clinical outcomes at discharge. Results: There were a total of 1,388,296 acute ischemic strokes between 2012 and 2014. Raking and Bayesian estimates of clinical data not recorded in administrative databases were estimated within 5 to 10% of the margins of reference values. Median weights for the raking method were 1.366 and the weights at the 99 th percentile were 6.881 with a maximum weight of 30.775. Median Bayesian weights were 1.329 and the 99 th percentile weights were 11.201 with a maximum weight of 515.689. Conclusions: Leveraging existing databases with patient registries to develop post-stratification weights is a reliable approach to estimate acute ischemic stroke epidemiology and monitoring for stroke quality of care nationally. Post-stratification weighting may be used as a basis for more advanced modeling relevant to understanding the burden of acute ischemic stroke and the quality of care delivered in U.S. hospitals. These methods may be applied to other diseases or settings to better monitor population health.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".