Extubation to High-Flow Nasal Cannula in Infants Following Cardiac Surgery: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract High-flow nasal cannula (HFNC) therapy is commonly used in the pediatric intensive care unit (PICU) for postextubation respiratory support. This hypothesis-generating retrospective cohort study aimed to compare postextubation PICU length of stay in infants extubated to HFNC and low flow oxygen (LF) in PICU following cardiac surgery. Of 136 infants (newborn to 1 year) who were intubated and mechanically ventilated in PICU following cardiac surgery, 72 (53%) were extubated to HFNC and 64 (47%) to LF. Compared with patients extubated to LF, those extubated to HFNC had significantly longer durations of cardiopulmonary bypass (152 vs. 109 minutes; p = 0.002), aortic cross-clamp (90 vs. 63 minutes; p = 0.003), and invasive mechanical ventilation (3.2 vs. 1.6 days; p < 0.001), although demographic and preoperative clinical variables were similar. No significant difference was observed in postextubation PICU length of stay between HFNC and LF groups in unadjusted analysis (3.3 vs. 2.6 days, respectively; p = 0.19) and after controlling for potential confounding variables (F [1,125] = 0.17, p = 0.68, R2 = 0.16). Escalation of therapy was similar between HFNC and LF groups (8.3 vs. 14.1%; p = 0.41). HFNC was effective as rescue therapy for six patients in the LF group requiring escalation of therapy. Need for reintubation was similar between HFNC and LF groups (8.3 vs. 4.7%; p = 0.5). Although extubation to HFNC was associated with a trend toward longer postextubation PICU length of stay and was successfully used as rescue therapy for several infants extubated to LF, our results must be interpreted with caution given the limitations of our study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".