Novel approaches to the prediction, diagnosis and treatment of cardiac late effects in survivors of childhood cancer: a multi-centre observational study
Bibliographic record
Abstract
BACKGROUND: Anthracycline-induced cardiac toxicity is a cause of significant morbidity and early mortality in survivors of childhood cancer. Current strategies for predicting which children are at greatest risk for toxicity are imperfect and diagnosis of cardiac injury is usually made relatively late in the natural history of the disease. This study aims to identify genomic, biomarker and imaging parameters that can be used as predictors of risk or aid in the early diagnosis of cardiotoxicity. METHODS: This is a prospective longitudinal cohort study that recruited two cohorts of pediatric cancer patients at six participating centres: (1) an Acute Cohort of children newly diagnosed with cancer prior to starting anthracycline therapy (n = 307); and (2) a Survivor Cohort of long-term survivors of childhood cancer with past exposure to anthracycline (n = 818). The study team consists of three collaborative cores. The Genomics Core is identifying genomic variations in anthracycline metabolism and in myocardial response to injury that predispose children to treatment-related cardiac toxicity. The Biomarker Core is identifying existing and novel biomarkers that allow for early diagnosis and prognosis of anthracycline-induced cardiac toxicity. The Imaging Core is identifying echocardiographic and cardiac magnetic resonance (CMR) imaging parameters that correspond to early signs of cardiac dysfunction and remodeling and precede global dysfunction and clinical symptoms. The data generated by the cores will be combined to create an integrated risk-prediction model aimed at more accurate identification of children who are most susceptible to anthracycline toxicity. DISCUSSION: We aim to identify genomic risk factors that predict risk for anthracycline cardiotoxicity pre-exposure and imaging and biomarkers that facilitate early diagnosis of cardiac injury. This will facilitate a personalized approach to identifying at-risk children with cancer who may benefit from cardio- protective strategies during therapy, and closer surveillance and earlier initiation of medications to preserve heart function after cancer therapy. TRIAL REGISTRATION: NCT01805778 . Registered 28 February 2013; retrospectively registered.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".