Automated control of fraction of inspired oxygen: is it time for widespread adoption?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Over the past two decades, numerous algorithms for automated control of the fraction of inspired oxygen (FiO2) have been developed and incorporated into contemporary neonatal ventilators and high-flow devices in an attempt to optimize supplemental oxygen therapy in preterm infants. This review explores whether current evidence is sufficient to recommend widespread adoption of automated oxygen control in neonatal care. RECENT FINDINGS: To date, 15 studies have compared automated versus manual control of FiO2 in preterm infants on respiratory support. This includes four new randomized cross-over trials published in the last 2 years. Available evidence consistently demonstrates a significant improvement in time spent within the target saturation range with automated FiO2 control. There are fewer episodes of severe hypoxemia and fewer manual FiO2 adjustments with automated oxygen control. Nursing workload may be reduced. However, no currently completed studies report on clinical outcomes, such as chronic lung disease or retinopathy of prematurity. SUMMARY: Automated oxygen control appears to be a reasonable option for FiO2 titration in preterm infants on respiratory support, if resources are available, and might substantially reduce nursing workload. Further randomized clinical trials to explore its effects on clinical outcomes are required.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".