Specialty teams for neonatal transport to neonatal intensive care units for prevention of morbidity and mortality
Bibliographic record
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: Primary Objective: To determine whether specialty transport teams compared with non‐specialty transport teams affect neonatal mortality and morbidity among newborn infants in need of transport to neonatal intensive care. Secondary Objective: To determine the effect on outcome depending on the types of specialty teams, gestational age, birth weight, disease severity and mode of transport for newborns requiring transfer to neonatal intensive care units (NICUs). Subgroups: Population subgroups: 1. Type of specialty teams: In various combinations consisting of at least two people of the following a. Medical ‐ neonatologist, neonatal trainee, paediatrician, others b. Nursing ‐ general nurse, neonatal specialist, neonatal nurse practitioner, midwife c. Combination 2. Gestational age: a. < 32 weeks b. > 32 weeks 3. Birth weight: a. < 1500 grams b. > 1500 grams 4. Disease severity prior to the arrival of the transport team at referring hospitals a. Respiratory support required i. Fraction of inspired oxygen (FiO2) ‐ < 0.40, > 0.40 ii. Mean airway pressure ‐ < 10 mmHg, > 10 mmHg iii. Oxygenation index ‐< 25, > 25 iv. Need for continuous positive airway pressure (CPAP) b. Hypoxic‐ischaemic encephalopathy (HIE) ‐ Sarnat Stage 1, 2 or 3 (Sarnat 1976) c. Congenital abnormalities 5. Mode of transport a. Road b. Air (helicopters or airplanes) 6. Level of perinatal care at referring site
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.005 |
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".