Invasive Fungal Infections in Neonates in Canada
Bibliographic record
Abstract
BACKGROUND: Neonatal fungemia is associated with adverse neonatal outcomes and higher overall healthcare expenditure. Our objective is to review the epidemiology of invasive fungal infections (IFIs) in neonates in Canada. METHODS: A retrospective cohort study using data collected by the Canadian Neonatal Network (CNN) was conducted. Using a nested matched cohort study design, risk factors and outcomes of neonates born <33 weeks gestation (n = 39,305) during 2003-2013 were compared between neonates diagnosed with an IFI during their stay to infection-free controls. RESULTS: Overall incidence of IFI among all admitted neonates was 0.22% (n = 286), while the incidence of IFI in the group of neonates born <33 weeks gestation was 0.64%. Of the isolates, 170 (59%) had Candida albicans and 59 (21%) had Candida parapsilosis. Risk factors for IFI were lower gestation, male sex, Apgar score <7 at 5 minutes, higher severity of illness score, maternal diabetes and vaginal birth. Neonates with IFI had higher odds of mortality [adjusted odds ratio (aOR): 1.60; 95% confidence interval (CI): 1.06-2.43], necrotizing enterocolitis (aOR: 2.97; 95% CI: 1.76-5.01) and severe retinopathy of prematurity (aOR: 2.15; 95% CI: 1.26-3.67). CONCLUSIONS: The overall incidence of IFI in neonates was low in Canada in comparison to other large population cohort studies; however, the mortality and morbidity remained high.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".