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Record W7027652480

A description of hepatitis C infection on Prince Edward Island

2018· article· en· W7027652480 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHepatitis C virusHepatitis CIntravenous drugEpidemiologyRetrospective cohort studyViral diseaseAction plan
DOInot available

Abstract

fetched live from OpenAlex

In Prince Edward Island (PEI), it was estimated that there were up to 800 people infected with Hepatitis C Virus (HCV) since testing began in 1991; however, the exact number of those still actively infected was not known. The purpose of this study was to determine (a) the prevalence of active HCV cases; (b) the number of acute/chronic cases at diagnosis; (c) the demographic/risk profile of HCV cases; and (d) the risk factors significant to the diagnosis of acute cases on PEI. This study used a descriptive, correlational, quantitative design using retrospective chart review data and laboratory data for all laboratory confirmed HCV cases living on PEI from 1991 to 2016. Approximately 430 cases remain actively infected on PEI. Intravenous drug use was the most prevalent risk factor, 65% of cases were diagnosed between 30 and 59 years of age, more males were diagnosed than females, and females (34 years) were diagnosed at a significantly younger age than males (37 years). Limited laboratory data allowed 388 cases to be diagnosed into acute and chronic cases. Eighteen percent were acute; 68% were chronic. The younger the case was in age the greater the probability of the case being acute. There are still many people on PEI who would benefit from HCV treatment. Development of a broader case definition for acute cases in PEI may improve the information available to identify at-risk people and plan action for prevention of HCV.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.889

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)0.0000.000

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 head0.223
Teacher spread0.216 · 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 designBench or experimental
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

Citations0
Published2018
Admission routes1
Has abstractyes

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