Predictors of respiratory depression at birth in the term infant
Bibliographic record
Abstract
OBJECTIVE: To evaluate predictive factors for respiratory depression at birth in infants >/=37 weeks. DESIGN: A population-based cohort study of respiratory depression at birth at term and post-term. SETTING: Nova Scotia, Canada. POPULATION: All 126 604 nonanomalous, singleton deliveries >/=37 weeks in cephalic presentation from 1988-2002. METHODS: An analysis of maternal, antenatal, intrapartum, and neonatal factors associated with respiratory depression at birth >/=37 weeks. MAIN OUTCOME MEASURES: A composite outcome of delay in initiating and maintaining respiration after birth, 5-minute Apgar score </= 3, or neonatal seizures due to hypoxic-ischaemic encephalopathy. RESULTS: The rate of respiratory depression at birth with delay in respiration was 5.2/1000, with Apgar </= 3 1.0/1000 live births, and with neonatal seizures 0.7/1000. A composite of any of the three respiratory depressions at birth criteria showed comparable low rates with spontaneous delivery (4.4/1000) and elective caesarean (4.8/1000). Compared with elective caesarean delivery, vacuum (13.2/1000, relative risk [RR] 3.97, P < 0.001), forceps (8.8/1000, RR 1.84, P= 0.003), failed vacuum (13.3/1000, RR 2.76, P= 0.005), failed forceps (33.3/1000, RR 6.93, P < 0.001), and caesarean in labour (17.0/1000, RR 3.54, P < 0.001) had significantly higher rates of the composite outcome. CONCLUSION: Overall, the rate of respiratory depression at birth in the term infant was low and the serious manifestation of seizures was less than 1 in 1000. There was a significant relationship between operative delivery in labour and respiratory depression at birth.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".