Outcomes of infants with hypoxic-ischemic encephalopathy during COVID-19 pandemic lockdown in Canada: a cohort study
Bibliographic record
Abstract
Abstract Purpose: To evaluate the change in the severity of hypoxic-ischemic encephalopathy (HIE) and associated morbidities between pre-and during COVID-19 pandemic periods in Canada. Methods: We conducted a retrospective cohort study extracting the data from level-3 NICUs participating in Canadian Neonatal Network (CNN). The primary outcome was a composite of death in the first week after birth and/or stage 3 HIE (Sarnat and Sarnat). Secondary outcomes included rate and severity of HIE among admitted neonates, overall mortality, brain injury on magnetic resonance imaging (MRI), neonates requiring resuscitation, organ dysfunction, and rates of therapeutic hypothermia (TH) usage. We included 1591 neonates with gestational age ≥36 weeks with HIE during the specified periods: pandemic cohort April 1st to December 31st of 2020; pre-pandemic cohort between April 1st to December 31st of 2017, 2018, and 2019. We calculated the odds ratio (OR) and confidence intervals (CI).Results: We observed no difference in the primary outcome (15% vs. 16%; OR 1.08; 95%CI 0.78-1.48), mortality in the first week of life (6% vs. 6%; OR 1.10, 95%CI 0.69-1.75), overall mortality, neonates requiring resuscitation, organ dysfunction, TH usage, and rate of brain injury. In the ad-hoc analysis, per 1000 live births, there was an increase in the rate of infants with HIE and TH use. Conclusions: Severity of HIE, associated morbidities and mortality were not significantly different during the pandemic compared to a pre-pandemic period in Canada. Anticipated risks and difficulties in accessing healthcare have not increased the mortality and morbidities in neonates with HIE in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".