The QTc-Bazett Interval in Former Very Preterm Infants in Adolescence and Young Adulthood is Not Different from Term-Born Controls
Bibliographic record
Abstract
Although relevant for precision pharmacovigilance, there are conflicting data on whether former preterm birth is associated with QT c-Bazett prolongation in later life. To explore QT c-Bazett interval differences between former preterm and/or extremely low birth weight (ELBW) cases and term-born controls in adolescence and young adulthood, we analyzed pooled individual data after a structured search on published cohorts. To test the absence of a QT c-Bazett difference, a non-inferiority approach was applied (one-sided, upper limit of the 95% confidence interval [CI] mean QT c-Bazett difference, 5 and 10 ms). We also investigated the impact of characteristics, either perinatal or at assessment, on QT c-Bazett in the full dataset (cases and controls). Data were reported as median and range. The pooled dataset contained 164 former preterm and/or ELBW (cases) and 140 controls born full-term from three studies. The median QT c-Bazett intervals were 409 (335–490) and 410 (318–480) ms in cases and controls. The mean QT c-Bazett difference was 1 ms, with an upper 95% CI of 6 ms ( p > 0.05 and p < 0.01 for 5 and 10 ms, respectively). In the full dataset, females had a significantly longer QT c-Bazett than males (415 vs. 401 ms; p < 0.0001). QT c-Bazett intervals are not significantly different between former preterm and/or ELBW cases and term-born controls, and we rejected a potential prolongation > 10 ms in cases. When prescribing QTc-prolonging drugs, pharmacovigilance practices in this subpopulation should be similar to the general public (NCT05243537).
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".