MétaCan
Menu
Back to cohort

Health care costs of persons with newly diagnosed hepatitis C virus: a population‐based, observational study

2008· article· en· W2095416281 on OpenAlexaffabout
Thu‐Ha Nguyen, Philip Jacobs, Anita Hanrahan, Nonie Fraser-Lee, Winnie Wong, Bonita E. Lee, Arto Öhinmaa

Bibliographic record

VenueJournal of Viral Hepatitis · 2008
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsCapital District Health AuthorityUniversity of AlbertaAlberta Health
Fundersnot available
KeywordsMedicineObservational studyHealth carePopulationHepatitis CFamily medicineMedical recordDemographyEnvironmental healthEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

The objective of this paper was to conduct an analysis of the health services costs for persons who have been diagnosed with hepatitis C, from the time of diagnosis. Data were based on 1230 persons diagnosed with hepatitis C in 1998 in the Capital Health region of Alberta. Identifiers and dates of diagnosis were sent to Alberta Health and Wellness where records were linked to those of physician visits and billings, as well as hospital (inpatient and outpatient) visit records. Costs were assigned to all visits, and data were analysed for one pre- and two post-diagnosis years. Total cost per person increased from $2630 (Canadian) to $3514 between the pre- and first post-diagnosis year. They then returned to $2694 in the second post-diagnosis year. Liver-related costs were a low portion of the total in all periods, though they increased following diagnosis. Mental-health related costs were the largest component. Observational data present a more balanced picture of the costs of persons with hepatitis C, though most current estimates are not based on such data. Our results indicate that, when analysed within the picture of the entire person, liver-related costs (which have been the focus of most studies to date) are the tip of the iceberg.

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 score0.033
Threshold uncertainty score0.973

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 integrity0.0000.001
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 head0.070
GPT teacher head0.363
Teacher spread0.293 · 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

Citations3
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Viral HepatitisSame topicHepatitis C virus researchFrench-language works237,207