Optimal Medical Therapy Attainment by Dialysis Status in the ISCHEMIA-CKD Trial
Bibliographic record
Abstract
Background: The efficacy of an aggressive multiple risk factor intervention approach - optimal medical therapy (OMT) - to reduce major adverse cardiovascular events in patients with CKD has not been tested. Objective: to examine OMT goal attainment in patients with CKD on dialysis (CKD-D) and non-dialysis CKD (CKD-ND) in the ISCHEMIA-CKD trial. Methods: OMT was recommended to all participants in ISCHEMIA-CKD. Longitudinal trajectories of individual OMT components (smoking cessation, systolic blood pressure (SBP) <140 mmHg, low density lipoprotein (LDL) cholesterol <70 mg/dL, high-intensity statin use, and aspirin use) were modeled over study follow-up. Covariateadjusted percentage point difference in each OMT goal achieved at 24 months between CKD-D and CKD-ND groups (% difference [95% credible interval (CrI)]) was estimated. Results: There were 415 CKD-D and 362 CKD-ND patients at baseline. CKD-D were younger (61 v 67 yrs, p<0.001) and less often diabetic (53% v 62%, p=0.023). CKD-D patients were 7.9 % (0.7%, 14.8%) more likely than CKD-ND to attain the SBP goal at 24 months (Figure). CKD-D patients were 22.7% (-33.3%, -11.4%) less likely to receive high-intensity statins. There was a steady and similar increase in proportional achievement of OMT during follow up. Conclusions: OMT improved over time in advanced CKD-ND and CKD-D. CKD-D achieved the SBP goal more than CKD-ND, yet CKD-D were less likely to be treated with high-intensity statin. Future studies should explore systemic and patient-related barriers to attainment of OMT in this high-risk cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".