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Canadian Study of Outcomes in Adalimumab Patients with Support for Adherence: Results from the COMPANION Study

2016· article· en· W2977246521 on OpenAlexaffabout
Martin G. Latour, Sebastien K. Gerega, Brad Millson, Louis Bessette, John K. Marshall, Gerald Lebovic, Michael Sung, Driss Oraichi, Tania Gaetano, Sandra Gazel, Marie-Claude Laliberté

Bibliographic record

VenueThe American Journal of Gastroenterology · 2016
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversité LavalSt. Michael's HospitalMerck Canada Inc. (Canada)McMaster UniversityABB (Canada)
Fundersnot available
KeywordsMedicineHazard ratioAdalimumabOdds ratioInternal medicineInfliximabLogistic regressionDiscontinuationProportional hazards modelRetrospective cohort studyPropensity score matchingConfidence intervalDisease

Abstract

fetched live from OpenAlex

Introduction: Adalimumab (ADA) is a TNF-alpha inhibitor indicated for use in various inflammatory autoimmune diseases including Crohn's disease (CD) and ulcerative colitis (UC). Patients receiving ADA in Canada are eligible to enroll in a patient support program (PSP) providing personalized services including tailored interventions. This retrospective cohort study assessed the impact of specific factors, including PSP services and patient characteristics, on persistence and adherence to ADA. Methods: A patient linkage algorithm based on probabilistic matching was developed to link the ADA PSP database to the IMS Health longitudinal pharmacy transaction database, LRx. Patients that started ADA therapy between July 2010 and August 2014 were selected and their prescriptions were evaluated for 12 months after the index date to calculate days until end of persistence (>90 days without therapy), censored for patients who remained on therapy through month 12. Cox proportional hazards modelling provided hazard ratios (HR) for the association between persistence and patient characteristics and PSP services. Adherence, measured by medication possession ratio (MPR), was calculated and multivariable logistic regression provided adjusted odds ratios (OR) for the relationship between high adherence (MPR >= 80%) and patient characteristics and PSP services. Results: The linkage algorithm yielded a final sample of 10,857 patients (4,230 CD, 347 UC). Statistically significant differences in the hazard rate of discontinuation and the odds of high adherence were identified across multiple variables. Relative to the 30-39 year category, older age groups had significantly greater odds of adherence (OR = 1.247, 1.234, 1.323, 1.411, p < 0.01). Patients receiving ongoing motivational interventions, in the form of nurse-provided phone calls, were 72% less likely to cease therapy when compared to those that did not receive the interventions (HR = 0.282, p < 0.0001) and were also more adherent (OR = 1.483, p < 0.0001). Treatment abandonment (failure to initiate therapy after enrolment in the PSP) was >80% more frequent in patients that did not receive a pre-ADA intervention (p < 0.0001). Conclusion: Ongoing motivational interventions, as provided by the ADA PSP, were found to have a large and statistically significant association to greater patient persistence and adherence over the first 12 months of treatment. These results may help refine interventions aiming at improving treatment adherence.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.036
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.372
Teacher spread0.330 · 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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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