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Record W2079785438 · doi:10.1089/bsp.2007.0056

Syndromic Surveillance for Influenzalike Illness

2008· article· en· W2079785438 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBiosecurity and Bioterrorism Biodefense Strategy Practice and Science · 2008
Typearticle
Languageen
FieldMedicine
TopicData-Driven Disease Surveillance
Canadian institutionsToronto Public HealthPublic Health OntarioInstitute of Population and Public HealthUniversity of OttawaPublic Health Agency of Canada
FundersPublic Health Agency of Canada
KeywordsMedicine

Abstract

fetched live from OpenAlex

Emergency department data are currently being used by several syndromic surveillance systems to identify outbreaks of natural or man-made illnesses, and preliminary results suggest that regular outbreaks might be detected earlier with such data than with traditional reporting. This article summarizes a retrospective study of 5 influenza seasons in Ottawa,Canada; time-series analysis was used to look for an association between consultation to the emergency department for influenzalike illness and the isolation of influenza virus in the community. The population studied included both children and adults consulting to 3 local hospitals. In 4 seasons, visits to the emergency department involving children younger than 5 years consulting mainly for fever and for respiratory symptoms peaked 1 to 4 weeks before the isolation of influenza virus in the community. If monitored regularly for the presence of key symptoms, pediatric hospitals might be efficient and cost-effective sentinels of influenza and of other infectious diseases.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.311
Teacher spread0.280 · 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