<i>Cronobacter</i>spp. (« <i>Enterobacter sakazakii</i> » sensu lato) : implication dans la contamination des préparations en poudre pour nourrissons et enfants en bas âge
Bibliographic record
Abstract
Species of the Cronobacter genus ("Enterobacter sakazakii" s. l.) are emergent food-borne pathogens that can cause rare but severe neonatal meningitis, bacteriaemia, and necrotizing enterocolitis. Preterm, low-birth-weight, and immuno-compromised infants exposed to these bacterial species are at particular risk. Over the last 50 years, the literature has reported, mainly in newborn children, more than one hundred cases of infection due to these pathogens. The objective of this review was to synthesize the recent advances in knowledge of species of the Cronobacter genus, in particular with regards to taxonomy, physiology, pathogenicity, clinical cases, the methods for detection, isolation, and characterization, and their presence in powdered formulae for infants and young children, which were identified as the main infection vector. Researchers and international public health authorities have explored the ways contamination occur to better control the risks of pathogen development. Appropriate analysis and control measures were implemented in areas processing powdered formulae for infants and young children, and caregivers and families were informed to undertake good hygienic practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".