Bibliographic record
Abstract
Preterm infants in neonatal intensive care units frequently require oxygen therapy. Clinicians are responsible for titrating oxygen to maximize the benefits and minimize the risks of this therapy. Studies have identified various toxic effects of oxygen on the developing tissues of the preterm infant; however, optimal target SpO(2) ranges have not been identified. Current trends in neonatology are focusing on defining optimal oxygen saturation ranges to improve infant outcomes and to decrease complications associated with the oxygen use. Consequently, research-based guidelines are being developed in neonatal intensive care units to guide oxygen administration. As target oxygen saturation ranges are developed, issues regarding health care professional compliance with these ranges have been identified. The specific reasons for this noncompliance have not been widely explored. However, factors such as nursing shortages, staffing issues, and a de-emphasis on staff education surrounding oxygen use have been offered as possible reasons. Understanding factors shaping clinical decision-making about oxygen titration is critical when designing policies and educational programs to change oxygen titration practice and ultimately improve patient outcomes. In this article, the literature outlining the importance of oxygen titration for preterm infants is reviewed. Discussion then focuses on factors that influence clinical decision-making and how these factors may influence decisions surrounding the use of oxygen for preterm infants.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".