QTc intervals at rest and during exercise assessed by group correction formulas in survivors of childhood acute lymphoblastic leukemia
Bibliographic record
Abstract
INTRODUCTION: Early signs of subclinical cardiac damage must be identified before they turn into clinical manifestations. Tailoring a formula is relevant for precise QTc evaluation in childhood acute lymphoblastic leukemia (ALL) survivors considering they are at risk of long-term cardiac problems. Therefore, we aim to develop group heart rate correction formulas for QT intervals in childhood ALL survivors at rest and during exercise, and to assess the applicability of these methods across a variety of risk groups exposed to diverse chemotherapy dosages. METHODS: Two hundred and fifty childhood ALL survivors in the PETALE study were classified into 3 groups depending on their prognostic risk group. ECG measurements (QT and RR intervals) were made at rest and during a cardiopulmonary exercise test. QT correction for heart rate was applied using 5 different formulas, which included 2 previously published formulas and 3 group-specific formulas for each sex. RESULTS: The QT/RR relation showed 2 different curves between rest and during exercise, which was worse for females. Group-specific QTc formulas allowed adequate heart rate-corrected QT interval, independently of the cumulative dose of doxorubicin received during treatment. Group-specific formulas showed significantly shorter QTc intervals than QTc from Bazett's formula. QTc (Bazett's formula) values surpassed the established clinical norm in 22 males (11%) and 22 females (11%), with a majority occurring during exercise, affecting 15 males (7.5%) and 10 females (5%). CONCLUSION: This study shows the applicability of personalized group correction of QT/RR data in childhood ALL survivors. Our comprehensive assessments (spanning rest, exercise, and recovery) is an effective approach for risk stratification of cardiac complications in childhood ALL survivors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".