Are Quality Improvements in the Get With The Guidelines-Stroke Program Related to Better Care or Better Data Documentation?
Bibliographic record
Abstract
BACKGROUND- Increased compliance with performance measures could reflect better care or better data documentation. We examined trends in the documentation of eligibility criteria, treatment contraindications, and missing data in the Get With The Guidelines-Stroke program to quantify their contribution to increased performance measure compliance. METHODS AND RESULTS- Data on 569 883 ischemic stroke admissions to 1028 GWTG-Stroke hospitals between April 2003 and September 2009 were obtained. Seven measures were examined: intravenous recombinant tissue plasminogen activator therapy, early antithrombotics, deep vein thrombosis prophylaxis, anticoagulants for atrial fibrillation/flutter, discharge antithrombotics, lipid therapy, and smoking cessation. Within each target population, the proportion of subjects treated, not treated, not treated because of contraindications, or with missing data were generated by calendar year. There were minimal changes in the size of the target populations for 6 of the measures; however, the size of the deep vein thrombosis prophylaxis population was reduced ≈5% in 2008 because of a format change to the data collection form. All measures showed significant increases in the proportion of eligible subjects treated across the study period. These increases occurred without major shifts in contraindications or missing data, with the exception of anticoagulation for atrial fibrillation/flutter where the increase occurred in conjunction with a decline in contraindications. Similar findings were seen when the data were examined by the duration of hospital participation in the program. CONCLUSIONS- These findings suggest that the majority of performance improvement in the Get With The Guidelines-Stroke program represent an increase in the number of patients with stroke treated and not changes to the underlying target populations or documentation of contraindications or missing data.
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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 | 0.000 |
| 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".