Hospital-related, maternal, and fetal risk factors for neonatal asphyxia and moderate or severe hypoxic-ischemic encephalopathy: a retrospective cohort study
Bibliographic record
Abstract
A previous large US study had documented an increased risk of asphyxia in small volume and rural hospitals. Our objective was to evaluate this in all hospitals in Alberta, a Canadian province. Retrospective cohort study of all singleton births ≥ 35-week gestation, in Alberta, from 2002–16 recorded in a perinatal database. Asphyxia was defined as intrapartum stillbirth or neonatal death from asphyxia or Neonatal Intensive Care Unit admission and at least two of the following: a. Apgar score of ≤ 5 at 10 minutes; b. mechanical ventilation or chest compressions for resuscitation within 10 minutes; c. cord pH 3600 annual births and Rural: The overall rate of neonatal asphyxia was 2.28 per 1000 births for the study period and was 2.5/1000 in the urban hospitals and 1.35/1000 in the rural hospitals, OR: 1.86 95% CI (1.58, 2.19). The rate of moderate or severe neonatal hypoxic-ischemic encephalopathy was 0.9/1000 and was not associated with urban hospital birth; OR: 1.12 95%CI (0.82, 1.53) hospital volume was also not associated with asphyxia or moderate or severe neonatal hypoxic-ischemic encephalopathy. This study observed similar rates of asphyxia and moderate or severe neonatal hypoxic-ischemic encephalopathy for rural and urban hospitals in Alberta and no association with hospital volume.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".