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Record W1983489842 · doi:10.1177/0272989x03261568

Estimating the Prognosis of Hepatitis C Patients Infected by Transfusion in Canada between 1986 and 1990

2004· article· en· W1983489842 on OpenAlexaffabout
Murray Krahn, John B. Wong, Jenny Heathcote, Linda Scully, Leonard B. Seeff

Bibliographic record

VenueMedical Decision Making · 2004
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineCirrhosisCohortNatural historyBlood transfusionHepatitis CHepatitis C virusConfidence intervalCohort studyLiver diseaseMarkov modelInternal medicineIntensive care medicinePediatricsMarkov chainVirologyVirusStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop a natural history model for chronic hepatitis C virus (HCV) infection to determine allocation of compensatory funds to Canadians who acquired HCV through the blood supply from 1986 through 1990. METHODS: A Markov cohort simulation model for HCV prognosis was developed, using content experts, published data, posttransfusion look-back data, and a national survey. RESULTS: The mortality rate in transfusees is high (46% at 10 years), although HCV-related deaths are rare. Only 14% develop cirrhosis at 20 years (95% confidence interval, 0%--44%), but 1 in 4 will eventually develop cirrhosis, and 1 in 8 will die of liver disease. CONCLUSIONS: This unique application of Markov cohort simulation and epidemiologic methods provides a state-of-the-art estimate of HCV prognosis and has allowed compensation decisions to be based on the best available evidence.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.043
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.017
GPT teacher head0.313
Teacher spread0.296 · 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

Citations48
Published2004
Admission routes2
Has abstractyes

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