Abstract WP313: Quality of Ischemic Stroke-related Care in Patients With Chronic Kidney Disease (CKD) on Dialysis: Findings From Get With the Guidelines-stroke
Bibliographic record
Abstract
Objectives: To assess the quality of ischemic stroke-related care based on American Heart Association (AHA) Get With The Guidelines (GWTG)-Stroke metrics for patients receiving dialysis compared to those with less severe or no CKD . Methods: The analysis is based on data from 232,236 Medicare-eligible fee-for-service patients admitted to 1581 GWTG-Stroke participating hospitals between January 2009 and December 2012. GFR was determined based on the MDRD study equation. Patients receiving dialysis were identified by ICD-9 diagnosis codes (V45.11 renal dialysis status, 585.6 end stage renal disease, V56.X encounter for dialysis and dialysis catheter care). Results: Compared to no CKD, dialysis patients received fewer guideline-based therapies (Table). Compared to other CKD stages, dialysis patients less often received VTE prophylaxis, timely treatment with IV-tPA, and defect free care. Treatment with early antithrombotics, discharge on antithrombotics, anticoagulation for atrial fibrillation, and statin use were lowest in patients with CKD stage 5. Conclusion: Among Medicare beneficiaries with acute ischemic stroke, guideline-adherent care is less frequent in patients on dialysis compared to those with less severe or no CKD. Additional work is needed to determine the reasons for these differences.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".