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Record W2586964501 · doi:10.1093/ecco-jcc/jjx002.801

P677 Impact of patient reported outcomes, coping strategies and psychosocial factors on medication adherence in inflammatory bowel disease

2017· article· en· W2586964501 on OpenAlexaffabout
C.-Y. Chao, Carolyne Lemieux, Waqqas Afif, Alain Bitton, Gary Wild, Talat Bessissow

Bibliographic record

VenueJournal of Crohn s and Colitis · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicinePsychosocialInflammatory bowel diseaseAnxietyUlcerative colitisInternal medicineQuality of life (healthcare)DiseasePhysical therapyDepression (economics)Coping (psychology)Psychiatry

Abstract

fetched live from OpenAlex

Background: Medication adherence is pivotal to the optimal management of inflammatory bowel disease (IBD) patients and this could be influenced by various patient related factors independent of disease course. This study aims to evaluate the impact of patient reported outcomes, coping strategies and psychosocial factors on adherence. Methods: We conducted a cross-sectional study on patients with Crohn's disease (CD) or ulcerative colitis (UC) at McGill IBD centre between September 2015 and March 2016. Patients were assessed for quality of life, disability and productivity using validated short IBD questionnaire (SIBDQ), IBD disability index (IBDDI) and work productivity assessment index (WPAI) respectively. Psychological assessment was performed using hospital anxiety and depression score (HADS). Brief COPE questionnaire were used for assessing coping strategies. Disease activity was determined by Harvey Bradshaw index (HBI >4) and partial Mayo score (PMS>2). Medication adherence was evaluated using the medication adherence questionnaire (poor adherence defined as MAQ>2) and the treating physician also independently provided their perception of individual patient adherence. Results were examined using descriptive and regression analysis. Results: 207 (144 CD/63 UC) patients, with median age of 39 (IQR 26) and 42.5% male, were included. 24.2% of patients had clinically active disease. 23.2% of patients were on immunomodulators and 52.7% on biologic therapy. Around one third of patients identified moderate to severe impairment on disability (31.3%), quality of life (33.3%) and productivity (29.1%); along with some degree of anxiety (32.9%) and depression (23.3%). Poor adherence was reported by 29.5% of patients and it is inadequately identified by treating physicians (r=0.19, p=0.009). Patients with poor quality of life, anxiety, depression and maladaptive coping behaviours were less likely to be adherent to therapy on univariate analysis (Table 1). Conversely adherence is associated with older age, biologic use, and marital status. Disease activity, duration, disability, education, employment and insurance status were not significantly associated with adherence. Age, marital status, biologic use and maladaptive behaviour remain significant on multi-variate analysis. Table 1. Medication adherence: Logistic regression analysis Conclusions: Selected patient characteristics and reported outcomes including age, biologic use, marital status and maladaptive behaviour may help to identify those patients at risk of medication non-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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.301
Teacher spread0.287 · 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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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