Oxygen for the delivery room respiratory support of moderate‐to‐late preterm infants. An international survey of clinical practice from 21 countries
Bibliographic record
Abstract
Abstract Aim The aim of this study was to determine clinician opinion regarding oxygen management in moderate‐late preterm resuscitation. Methods An anonymous online questionnaire was distributed through email/social messaging platforms to neonatologists in 21 countries (October 2020‐March 2021) via REDCap. Results Of the 695 respondents, 69% had access to oxygen blenders and 90% had pulse oximeters. Respondents from high‐income countries were more likely to have oxygen blenders than those from middle‐income countries (72% vs. 66%). Most initiated respiratory support with FiO2 0.21 (43%) or 0.3 (36%) but only 45% titrated FiO2 to target SpO2. Most (89%) considered heart rate as a more important indicator of response than SpO2. Almost all (96%) supported the need for well‐designed trials to examine oxygenation in moderate‐late preterm resuscitation. Conclusion Most clinicians resuscitated moderate‐late preterm infants with lower initial FiO2 but some cannot/will not target SpO2 or titrate FiO2. Most consider heart rate as a more important indicator of infant response than SpO2.Large and robust clinical trials examining oxygen use for moderate‐late preterm resuscitation, including long‐term neurodevelopmental outcomes, are supported amongst clinicians.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".