Automated oxygen control in preterm babies on respiratory support: protocol for a randomised crossover trial
Bibliographic record
Abstract
INTRODUCTION: Respiratory support is frequently needed for babies admitted to the neonatal intensive care unit. Among them, preterm babies are most likely to have issues of respiratory distress, and they may need invasive or non-invasive breathing support. Providing respiratory support, keeping the oxygen saturation (SpO2) in the target range (TR) and preventing abnormal high and low oxygen levels should be the aim of providing respiratory therapy. Usually, this control is achieved by manual adjustment of FiO2 (fraction of inspired oxygen) by bedside staff nurses to keep SpO2 in TR. However, the latest ventilators have automated oxygen control devices that adjust the FiO2 to keep SpO2 in TR. This study protocol is prepared to assess the effectiveness of automated versus manual oxygen control in keeping SpO2 in TR. METHODS AND ANALYSIS: This is a single-centre, non-blinded, randomised crossover trial that aims to recruit 26 preterm babies who may need invasive or non-invasive respiratory support. The 12-hour periods of automated oxygen control by ventilator will be compared with 12 hours of manual oxygen control by bedside staff nurse. The primary outcome will compare both interventions and will assess their efficacy to keep SpO2 in TR. Secondary outcomes will compare abnormal high and low SpO2 levels, and number and duration of fluctuations in both interventions. Median FiO2 values and median number of manual adjustments of FiO2 will also be compared. Secondary outcomes will also look for the impact of sedative and respiratory stimulant medications on target oxygen saturation. ETHICS AND DISSEMINATION: The ethics review committee at Aga Khan University Hospital Karachi has given ethical approval for this trial (approval number: 2024-10189-30775). Results from this trial will be published in journals. TRIAL REGISTRATION NUMBER: NCT06622161.
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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 | 0.034 | 0.038 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".