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Record W1970746510 · doi:10.1080/00365540500525161

Enhanced surveillance of newly acquired hepatitis C virus infection in Canada, 1998 to 2004

2006· article· en· W1970746510 on OpenAlexafffundabout
Hong‐Xing Wu, Jun Wu, Tom Wong, Tracey Donaldson, Katherine Dinner, Anton Andonov, Jessica Ip Chan, Barbara Moffat, Beverley Baptiste, Janet Furseth, Darlene Poliquin, Grlica Bolesnikov, Antonio Giulivi, Shirley Paton

Bibliographic record

VenueScandinavian Journal of Infectious Diseases · 2006
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsOttawa Public HealthAlberta Health ServicesPublic Health Agency of CanadaVancouver Coastal HealthCapital District Health AuthorityBC Centre for Disease ControlCanadian Science Centre for Human and Animal Health
FundersPublic Health Agency of Canada
KeywordsIncidence (geometry)MedicineTransmission (telecommunications)Hepatitis C virusHepatitis CViral diseaseEpidemiologyHepatitisRisk factorDiseaseVirologyInternal medicineVirus

Abstract

fetched live from OpenAlex

The purpose of this study was to determine trends in disease incidence and recent patterns of hepatitis C virus (HCV) transmission in Canada, using the Enhanced Hepatitis Strain Surveillance System (EHSSS). Demographic, clinical, and potential risk factor information on newly acquired HCV infection, from 1998 to 2004, was collected using standardized questionnaires. During this time period, the reported incidence of newly acquired HCV infection declined by 36.4% from 3.3 cases per 100,000 in 1998, to 2.1 cases per 100,000 in 2004. The disease incidence peaked at 15 to 39 y of age, confirming injecting drug use as the most frequently reported route of transmission. The proportion of cases attributed to health care-acquired HCV infection decreased over this time period. Although the incidence of newly acquired HCV infection in the EHSSS was found to be declining, hepatitis C remains an important public health threat to Canadians. Prevention efforts for HCV should focus on injection drug use, especially for people aged 15 to 39 y.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.036
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.261
Teacher spread0.254 · 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 teacher head, 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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