Variations in Oxygen Saturation Targeting, and Retinopathy of Prematurity Screening and Treatment Criteria in Neonatal Intensive Care Units: An International Survey
Bibliographic record
Abstract
BACKGROUND: Rates of retinopathy of prematurity (ROP) and ROP treatment vary between neonatal intensive care units (NICUs). Neonatal care practices, including oxygen saturation (SpO2) targets and criteria for the screening and treatment of ROP, are potential contributing factors to the variations. OBJECTIVES: To survey variations in SpO2 targets in 2015 (and whether there had been recent changes) and criteria for ROP screening and treatment across the networks of the International Network for Evaluating Outcomes in Neonates (iNeo). METHODS: Online prepiloted questionnaires on treatment practices for preterm infants were sent to the directors of 390 NICUs in 10 collaborating iNeo networks. Nine questions were asked and the results were summarized and compared. RESULTS: Overall, 329/390 (84%) NICUs responded, and a majority (60%) recently made changes in upper and lower SpO2 target limits, with the median set higher than previously by 2-3% in 8 of 10 networks. After the changes, fewer NICUs (15 vs. 28%) set an upper SpO2 target limit > 95% and fewer (3 vs. 5%) a lower limit < 85%. There were variations in ROP screening criteria, and only in the Swedish network did all NICUs follow a single guideline. The initial retinal examination was carried out by an ophthalmologist in all but 6 NICUs, and retinal photography was used in 20% but most commonly as an adjunct to indirect ophthalmoscopy. CONCLUSIONS: There is considerable variation in SpO2 targets and ROP screening and treatment criteria, both within networks and between countries.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".