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Record W2211112986 · doi:10.1155/2000/642707

Prediction of Hepatitis C Burden in Canada

2000· article· en· W2211112986 on OpenAlexafffundvenueabout
Shimian Zou, Martin Tepper, Susie El Saadany

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2000
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsSaskatchewan Disease Control Laboratory
FundersUniversity of Toronto
KeywordsMedicineHepatitis CCirrhosisHepatitis C virusHepatitisLiver diseaseNatural historyDiseaseHepatocellular carcinomaInternal medicineHepatitis ADisease controlVirologyEnvironmental healthVirus

Abstract

fetched live from OpenAlex

To assess the risk of hepatitis C in Canada and to predict the burden that this disease may pose to the Canadian society in the near future, expected numbers of persons at different stages of the disease currently and in the next decade were estimated by simulation using a published hepatitis C natural history model with no treatment effect being applied. Based on the estimate of 240,000 persons who are currently infected with the hepatitis C virus in Canada, the simulation analysis demonstrated that the number of hepatitis C cirrhosis cases would likely increase by 92% from 1998 to the year 2008. It was also projected that the number of liver failures and hepatocellular carcinomas related to hepatitis C would increase by 126% and 102%, respectively, in the next decade. The number of liver-related deaths associated with hepatitis C is expected to increase by 126% in 10 years. The medical and social care systems in Canada may not be ready to support these large increases. These results highlight the importance of both the control of disease progression of hepatitis C virus-infected persons and the primary prevention of hepatitis C infections in Canada.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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.

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

Citations82
Published2000
Admission routes4
Has abstractyes

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