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Record W2794036127 · doi:10.1111/liv.13708

Ribavirin dose management in <scp>HCV</scp> patients receiving ombitasvir/paritaprevir/ritonavir and dasabuvir with ribavirin

2018· article· en· W2794036127 on OpenAlexaff
Jordan J. Feld, David Bernstein, Ziad Younes, Hans Van Vlierberghe, Lois Larsen, Fernando Tatsch, Péter Ferenci

Bibliographic record

VenueLiver International · 2018
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsToronto Liver CentreToronto General Hospital
FundersAbbVie
KeywordsOmbitasvirDasabuvirRibavirinParitaprevirRitonavirMedicineInternal medicineGastroenterologyVirologyHepatitis C virusViral loadVirus

Abstract

fetched live from OpenAlex

BACKGROUND & AIMS: Some individuals with hepatitis C virus infection treated with direct-acting antivirals require ribavirin to maximize sustained virological response rates. We describe the clinical management of ribavirin dosing in hepatitis C virus-infected patients receiving ombitasvir/paritaprevir/ritonavir and dasabuvir with ribavirin. METHODS: We performed a post hoc analysis of patients receiving ombitasvir/paritaprevir/ritonavir and dasabuvir with ribavirin for 12 or 24 weeks in six phase 3 trials. Multivariate stepwise logistic regression models assessed predictors associated with ribavirin dose adjustments and with developing anaemia. RESULTS: Of 1548 patients, 100 (6.5%) modified ribavirin dose due to haemoglobin declines, of which 99% achieved sustained virological response at 12 weeks post-treatment. Median time to first ribavirin dose reduction was 37 days. Low baseline haemoglobin was significantly associated with an increased risk of requiring ribavirin dose modification (odds ratio: 0.618 [0.518, 0.738]; P < .001) and developing anaemia (odds ratio: 0.379 [0.243, 0.593]; P < .001). CONCLUSIONS: Ribavirin dose reductions were infrequent, occurred early in treatment, and did not impact sustained virological response at 12 weeks post-treatment. Patients with low baseline haemoglobin should be monitored for on-treatment anaemia.

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 score0.121
Threshold uncertainty score0.877

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 head0.022
GPT teacher head0.304
Teacher spread0.282 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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