Nutrition and listeriosis during pregnancy: a systematic review
Bibliographic record
Abstract
Listeriosis is a rare but severe foodborne illness which is more common in populations such as pregnant women, and can result in serious complications including miscarriage, prematurity, maternal and neonatal sepsis, and death in the newborn. Population recommendations exist for specific foods and food preparation practices to reduce listeriosis risk during pregnancy. The aim of the present systematic review was to assess the association between listeriosis and these practices during pregnancy to confirm appropriateness of these recommendations. We searched MEDLINE, Embase, CINAHL Plus, Web of Science Core Collection, included articles' references, and contacted clinical experts. All databases were searched until July 2017. Case-control and cohort studies were included which assessed pregnant women or their newborn offspring with known listeriosis status and a nutritional exposure consistent with international population recommendations for minimising listeriosis. Outcomes included listeriosis with or without pregnancy outcomes. Risk of bias was assessed through the Newcastle-Ottawa Scale. Results were described narratively due to clinical heterogeneity in differences in nutritional exposures. Eleven articles comprising case-control or cross-sectional studies met the inclusion criteria. Cases of maternal, fetal or neonate listeriosis were more likely to have consumed high-risk dairy products, meat products or some fruits during pregnancy in comparison with women without listeriosis. Cases of listeriosis were more likely to have consumed foods that are highlighted in population guidelines to avoid to minimise listeriosis in comparison with those without listeriosis during pregnancy. Further research is warranted assessing means of improving the reach, uptake and generalisability of population guidelines for reducing listeriosis during pregnancy.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".