Enhancing Prediction of Cancer Therapy-Related Cardiomyopathy From Surveillance Echocardiograms
Bibliographic record
Abstract
BACKGROUND: Early echocardiographic indicators of cardiac remodeling may enhance cardiomyopathy risk prediction in childhood cancer survivors (CCS). OBJECTIVES: The objective of the study was to assess whether influential echocardiographic measures can be combined to develop a robust cardiomyopathy risk prediction model in CCS. METHODS: Multicenter retrospective study of ≥1-year CCS with digitally archived surveillance echocardiograms, enrolled cardiomyopathy cases (left ventricular [LV] fractional shortening ≤28% or LV ejection fraction ≤50% on ≥2 occasions) and noncases (≥5-year CCS who maintained fractional shortening ≥ 30% and ejection fraction ≥55% without initiation of cardiac medications). Echocardiograms were centrally quantitated in a blinded fashion. Least absolute shrinkage and selection operator regression identified the most influential 2-year predictors of cardiomyopathy among 27 echocardiographic parameters. Logistic regression was used to generate ORs with 95% CIs. Estimates were applied to the training and test data sets to generate area under the receiver operating characteristic curves (AUC). RESULTS: : 1.3; 95% CI: 1.1-1.6) were strongly predictive of cardiomyopathy. AUCs were similar if cancer treatment exposures were included. CONCLUSIONS: Early abnormalities in echocardiographic parameters of structure and function predict subsequent cardiomyopathy in CCS and can identify high-risk survivors who warrant early intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".