PS-278 Automated Versus Manual Fio2 Control At Different Saturation Targets In Preterm Infants: Abstract PS-278 Table 1
Bibliographic record
Abstract
Background Preterm infants spend only 50% of time within the target oxygen saturation (SpO2) during manual FiO2 control (M-FiO2). Automated FiO2 control (A-FiO2) improves SpO2 targeting but it is uncertain if this applies to different SpO2 target ranges and during non-invasive support (NIVS) and mechanical ventilation (MV). Objective To compare the efficacy of A-FiO2 vs M-FiO2 in keeping two different SpO2 targets during NIVS or MV. Design/methods Preterm infants on FiO2 >0.21 receiving NIVS or MV were randomised to SpO2 targets 89–93% or 91–95% and underwent M-FiO2 and A-FiO2 for 24 h each, in random sequence. Results 80 infants (GA:26 w, age:18 d) were included (NIVS = 48, MV = 32). Time within target increased and below target decreased during A-FiO2 compared with M-FiO2, especially in the lower target range. There was a reduction in time and hypoxemia episodes with SpO2 < 80% during A-FiO2. Outcomes did not differ between NIVS or MV. Conclusions Automated FiO2 control improved SpO2 targeting across different SpO2 ranges and reduced hypoxemia with less workload during both NIVS and MV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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".