Clinical prevalence and outcome of cardiovascular events in the first 100 days postallogeneic hematopoietic stem cell transplant
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Recent advances in allogeneic hematopoietic stem cell transplant (HSCT) have allowed us to offer HSCT to older, advanced disease patients with more co-morbidities. Cardiovascular toxicity post-transplant is a major concern due to the increased risk of mortality. Few studies have examined the prevalence of CV events including CAD (MI, angina, PCI, CABG, CHF, arrhythmias), HTN, stroke/TIA, and death in the first 100 days post-transplant. PATIENTS: We assessed the impact of pretransplant MUGA results in predicting postallogeneic HSCT CV events and overall survival in the first 100 days, and whether or not transient anthracycline-induced cardiomyopathy or cumulative anthracycline dose affected overall survival. This retrospective, cohort study included 665 patients with a median age of 52 years who underwent HSCT from 2009 to 2015. RESULTS: The most frequent CV event in the first 100 days post-HSCT was arrhythmia seen in 2.9% of patients followed up by CHF (12.3%), MI (9%), and angina (8%). Two patients had PCI, and both survived the first 100 days. Cardiovascular risk factors predict for a poor MUGA scan but not survival. Higher dose anthracycline pretransplant predicted for a poor outcome. CONCLUSION: A history of CV disease, MI, or CAD was the most important predictive of CV events, P-value = .00002. 88.6% survived the first 100 days. Patients with an EF < 50% had a significant likelihood of having a CV event compared to patients with an EF > 60% (OR = 5.3, 95% CI [1.6-18.1], P = .0219). Cumulative anthracycline dose did not have a significant impact on overall survival.
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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 | 0.003 | 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 it