The effects of nasal continuous positive airway pressure and high flow nasal cannula on heart rate variability in extremely preterm infants after extubation: A randomized crossover trial
Bibliographic record
Abstract
BACKGROUND: NCPAP and High flow nasal cannula (HFNC) are common modes of non-invasive respiratory support used after extubation. Heart rate variability (HRV) has been demonstrated as a marker of well-being in neonates and differences in HRV were described in preterm infants receiving respiratory care. The objective was to investigate the effects of NCPAP and HFNC on HRV after extubation. METHODS: Randomized crossover trial in infants with birth weight (BW) ≤1250 g after undergoing their first elective extubation. ECG recordings were performed during 45 min while on HFNC and nasal continuous positive airway pressure (NCPAP). Time domain, non-linear, and frequency domain parameters were calculated and compared during HFNC and NCPAP using paired nonparametric tests. A secondary analysis was performed in the subgroup of infants that were successfully extubated. RESULTS: Thirty infants with median [range] gestational age of 27 weeks [24.1-29.3] and BW of 930 g [610-1220] were studied at 5 days [1-39] of age. No differences in HRV parameters were observed between HFNC and NCPAP. In the secondary analysis, infants successfully extubated (n = 27) had a significantly higher HRV during HFNC for some time domain parameters. For instance, the standard deviation of the RR intervals (SDRR) was more likely to be higher during HFNC compared to NCPAP (HFNC: 18/27 vs NCPAP: 9/27, P = 0.017) . CONCLUSION: During the first hours after extubation, no differences in HRV were detected between HFNC and NCPAP in the overall cohort. However, a significantly higher HRV was noted during HFNC in the subgroup of infants successfully extubated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".