Outcomes of Preterm Infants Admitted to Canadian NICUs Before and During the SARS-COV Pandemic
Bibliographic record
Abstract
Background: To better elucidate the impact of the SARS-COV pandemic on neonatal outcomes, we compared the health outcomes of infants born preterm requiring care in a Canadian NICU before and during the SARS-COV pandemic. Methods: Using a retrospective cohort study, infants born between 23 and 32 weeks gestation who were admitted to tertiary Canadian NICUs before and during the pandemic were included. A total of 7280 infants were in the pre-pandemic cohort (admitted 1 April 2018–31 December 2019), and 7088 infants were in the pandemic cohort (admitted 1 April 2020–31 December 2021). The primary outcomes included major morbidity or mortality rates. Care strategies and treatments were compared across the two periods. The relative risk (RR) for the pandemic period, compared to the pre-pandemic period, was calculated using a Poisson regression model, adjusted for identified risk factors. Results: There were no significant differences in infant characteristics between the pre-pandemic and pandemic cohorts. The risk of mortality or major morbidity was comparable before and during the pandemic (37% pre-pandemic, 36% pandemic; RR = 1.01, 95% CI 0.92, 1.01). Individual risks for morbidity and mortality did not differ significantly between periods. There was a clinically significant decline in the receipt of the mothers’ own milk exclusively at discharge during the pandemic (45% before and 37% during; RR 0.85, 95% CI 0.68, 1.06). Conclusions: There were no significant differences in major morbidity or mortality rates in preterm infants between pre-pandemic and pandemic cohorts in Canadian NICUs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".