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Record W2280465923 · doi:10.1155/2006/958191

Syndromic Surveillance of <i>Norovirus</i> Using over the Counter Sales of Medications related to Gastrointestinal Illness

2006· article· en· W2280465923 on OpenAlexafffundabout
Victoria L. Edge, Frank Pollari, Lai King, Pascal Michel, Scott A. McEwen, Jeffrey B. Wilson, Michael Jerrett, Paul Sockett, S.W. Martin

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2006
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsMcMaster UniversityUniversity of GuelphPublic Health Agency of Canada
FundersPublic Health Agency of Canada
KeywordsNorovirusOver-the-counterMedicinePsychological interventionEnvironmental healthOutbreakVirologyMedical prescriptionPharmacologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess whether over-the-counter (OTC) sales of gastrointestinal illness (GI)-related medications are associated with temporal trends of reportable community viral, bacterial and parasitic infections. METHODS: The temporal patterns in weekly and seasonal sales of nonprescription products related to GI were compared with those of reportable viral, bacterial and parasitic infections in a Canadian province. RESULTS: Temporal patterns of OTC product sales and Norovirus activity were similar, both having highest activity in the winter months. In contrast, GI cases from both bacterial and parasitic agents were highest from late spring through to early fall. CONCLUSIONS: Nonprescription sales of antidiarrheal and antinauseant products are a good predictor of community Norovirus activity. Syndromic surveillance through monitoring of OTC product sales could be useful as an early indicator of the Norovirus season, allowing for appropriate interventions to reduce the number of infections.

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)0.000
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 score0.253
Threshold uncertainty score0.510

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.262
Teacher spread0.256 · 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

Citations30
Published2006
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Infectious Diseases and Medical MicrobiologySame topicViral gastroenteritis research and epidemiologyFrench-language works237,207