10 The QTc interval in former very preterm infants is not different from term-born controls
Bibliographic record
Abstract
<h3>Introduction</h3> There are conflicting data on whether former preterm birth is associated with QTc-Bazett prolongation in later life. <h3>Methods</h3> To explore QTc-Bazett interval differences between former preterm and/or extreme 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 QTc-Bazett difference, a non-inferiority approach was applied (one-sided, upper limit of the 95% CI mean QTc-Bazett difference, 5 and 10 ms). We also investigated the impact of characteristics on QTc-Bazett . <h3>Results</h3> The pooled dataset contained 164 preterms and/or ELBW (cases) and 140 controls from 3 studies. The median QTc-Bazett intervals were 409 (335–490) and 410 (318–480) ms in cases and controls. The mean QTc-Bazett difference was 1 ms, upper CI 95% of 6 ms (p=0.1015 and 0.0019 for 5 and 10 ms respectively). In the full dataset, females had a significantly longer QTc-Bazett than males (415 vs. 401 ms, p<0.0001), and there was a significant, but weak correlation (Spearman’s 0.151, p=0.0377) between QTc-Bazett and plasma phosphate. <h3>Conclusions</h3> QTc-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.
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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) | 0.010 | 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".