Noninvasive High-Frequency Oscillatory Ventilation: A Retrospective Chart Review
Bibliographic record
Abstract
Objective This study aimed to review the feasibility of nasal high-frequency oscillatory ventilation (NHFOV) in preventing reintubation in preterm infants. Study Design This is a retrospective cohort study of all premature newborn infants placed on NHFOV in a single-center neonatal intensive care unit. Results Twenty-seven patients (birth weight: 765 ± 186 g, gestational age: 28 ± 2 weeks) were commenced on NHFOV on 32 occasions. NHFOV was used immediately postextubation as the primary mode of noninvasive ventilation (NIV; prophylaxis) in 10 of 32 occasions and as “rescue” (failure of NCPAP or biphasic CPAP) in 22 of 32 occasions. Treatment with NHFOV was successful in 22 occasions (69%) while on 10 occasions (31%) reintubation was required within 72 hours. In the rescue group, there was significant reduction in the mean (standard deviation [SD]) number of apneas (0.9 ± 1.07 vs. 0.3 ± 0.29, p < 0.005), but there were no significant changes in the PCO2 level (52 [ ± 9.8] vs. 52 [ ± 8.6] mm Hg, p = 0.8), or the FiO2 requirement (0.39 ± 0.19 vs. 0.33 ± 0.10, p = 0.055) before and after commencing NHFOV, respectively. Conclusion The use of NHFOV is feasible as a prophylactic or rescue mode of NIV following extubation and was associated with decrease in the number of apneas without significant changes in PCO2 or oxygen requirements. A well-designed randomized control trial is needed to determine the indications, clinical outcomes, and safety of this treatment modality. Key Points
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".