MétaCan
Menu
Back to cohort
Record W2515313589 · doi:10.1002/hep.28215

Poster Session 1: Practice Issues; Signal Transduction and Nuclear Receptors

2015· article· en· W2515313589 on OpenAlexaff
Aisling Considine, Suman Verma, Kath Oakes, Kate Childs, Sarah Knighton, Andrew Ayers, Abid Suddle, Kosh Agarwal, Colina Yim, Cheryl Dale, Geri Hirsch, Jo‐Ann Ford, Caro- Lyn Klassen, Carolyn Klassen, Heather Johnson, Emily M. Graham, Michael A. Dunn, Kapil Chopra, Anna Moles, Jacqueline Butterworth, Jill V. Hunter, Ana M. Sánchez, Dina Tiniakos, Derek A. Mann, Fiona Oakley, Neil D. Perkins

Bibliographic record

VenueHepatology · 2015
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsRoche (Canada)Vancouver General HospitalLondon Health Sciences CentreCapital District Health AuthorityUniversity Health Network
Fundersnot available
KeywordsSession (web analytics)Signal transductionReceptorTransduction (biophysics)SIGNAL (programming language)Cell biologyNeuroscienceComputational biologyMedicinePsychologyBiologyComputer scienceInternal medicineBiophysicsWorld Wide Web

Abstract

fetched live from OpenAlex

Background: The advent of directly acting anti-virals (DAA) has led to simplified HCV regimens with increased efficacy rates and better tolerability.The high adherence seen in clinical trials may not translate to real life populations.Optimal adherence is critical to protect patients from treatment failure and resistance.Aim: To identify potential factors which may contribute to sub-optimal adherence in a population with decompensated cirrhosis.Methods: HCV patients eligible for access to a 12 week treatment regimen under a UK NHSE mandated access scheme were consented to complete a baseline questionnaire capturing data on sociodemographics, clinical status and perceptions of illness.Adherence assessments at treatment weeks 4, 8 and 12 with pill counts and a Morisky Medication Adherence Scale were undertaken.Suboptimal adherence (SA) was defined as any report or pill count which indicated a delayed (>2hrs) or missed dose of anti-viral.Descriptive statistic and multivariate regression methods were utilised for analysis.Results: 80% (n=47) of the cohort had reached SVR 12 and are included in the adherence analysis.85%received sofosbuvir/ledipasvir and 15% sofosbuvir with daclatasvir.All patients received ribavirin and had been attending specialist hepatology services for >12 months.43%(n=20) of patients demonstrated SA during their treatment.Multivariate analysis demonstrated that any non-attendance during treatment and a 'limited support network' predicted higher rates of SA (p<0.05).In the SA group, 80% were taking >3 medications (vs 74% in adherent group).The average MELD score was 15 in the SA group (vs 13 in adherent group).80% of SA was associated with symptoms secondary to disease stage not reflected by the MELD.SA secondary to side effects of therapy was not identified.85% of the SA group achieved SVR12.Failure to achieve SVR was seen in those with SA adherence at all assessment points (weeks 4, 8 and 12).Conclusions: Our initial results demonstrate that despite an intensive adherence focused multi-disciplinary approach, SA occurs in decompensated HCV cirrhotic patients.Acceptable SVR rates were achieved in this population.Further research is warranted to develop strategies which maximise adherence in all HCV populations.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.222
Threshold uncertainty score0.744

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.2220.084

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 head0.028
GPT teacher head0.356
Teacher spread0.328 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueHepatologySame topicLung Cancer Treatments and MutationsFrench-language works237,207