MétaCan
Menu
Back to cohort
Record W3124653581 · doi:10.3390/pathogens10020107

Control of Raw Pork Liver Sausage Production Can Reduce the Prevalence of HEV Infection

2021· article· en· W3124653581 on OpenAlexaboutno aff
Paolo Ripellino, Enea Pianezzi, G Martinetti, Cinzia Zehnder, Barbara Mathis, Petra Giannini, Nicola Forrer, Giorgio Merlani, Harry R. Dalton, Orlando Petrini, Florian Bihl, Stefano Fontana, Claudio Gobbi

Bibliographic record

VenuePathogens · 2021
Typearticle
Languageen
FieldMedicine
TopicHepatitis Viruses Studies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePublic healthEnvironmental healthOutbreakPublic health interventionsPsychological interventionQuarter (Canadian coin)PopulationVirologyGeography

Abstract

fetched live from OpenAlex

After an acute hepatitis E (HEV) outbreak in Southern Switzerland, in January 2017 the local public health authorities started an active program of food chain control and public education. In this retrospective study, we analysed all laboratory-confirmed acute cases of HEV infection diagnosed between 2014 and 2020. In the period before the public health intervention, the number of cases increased steadily from 2014 (4 of 40 tests, 10%) reaching a peak in the last quarter of 2016 (42 of 285 tests, 14.7 %). Afterwards, the number of positive cases decreased steadily, reaching its lowest value (0.3%) in the second quarter of 2019. There was a statistically significant difference between the frequency of positive cases and period of testing, i.e., before and after the introduction of the public health interventions. Our study shows that active public health measures to control sausages containing raw pork liver can reduce the prevalence of HEV infection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.295
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venuePathogensSame topicHepatitis Viruses Studies and EpidemiologyFrench-language works237,207