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O-015 YI The Impact of Clinic Visits and Disease Activity on Adherence Rates in Children With Inflammatory Bowel Diseases

2014· article· en· W2332088586 on OpenAlexaff
Cheryl Kluthe, Huynh Hien, Matthew Carroll, Spady Donald, Eytan Wine, Tsui Jenkin

Bibliographic record

VenueInflammatory Bowel Diseases · 2014
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of AlbertaStollery Children's Hospital
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseUlcerative colitisMedical prescriptionInternal medicineDiseasePharmacy

Abstract

fetched live from OpenAlex

Medication non-adherence is a challenging issue in pediatric patients with inflammatory bowel diseases. Poor adherence often results in disease flare-ups, disease complications, therapy escalation and need for corticosteroids. Patients who have more frequent follow up appointments have greater medication adherence. By understanding why patients are non-adherent, we can educate patients and overcome these barriers. A retrospective chart review of patients attending the pediatric IBD clinic at the Stollery Children’s Hospital from January 2012 to December 2013 was completed. Disease activity scores were based on PCDAI for Crohn’s Disease (CD) and PUCAI for Ulcerative Colitis (UC). Medication adherence was calculated by the actual number of prescription days filled, compared to days prescribed, as provided by PIN provincial pharmacy database. The number of blood tests performed compared to the clinic’s protocol determined blood work compliance. Association was established between disease activity score at each clinical visit, percentage of prescriptions filled, blood work completed, rural/urban residence and whether patients were in steroid-free remission for the preceding 6 months at the time of data collection. One hundred and thirteen patients were reviewed with 71 patients diagnosed with CD and 42 (include 1 IBDU) were UC. 44% were female. The mean age of diagnosis for CD was 10.7 years, with a mean PCDAI score of 21 and the age for UC was 8.75 years, with a mean PCDAI score of 31. UC patients were diagnosed at a younger age (P = 0.011). Anti-TNF adherence, defined as administration at or before schedule date, was 85%. Immunomodulator adherence rate was 77%. Methotrexate adherence was better than Azathioprine in CD (86.73% versus 74.86%, respectively; P = 0.0523). 5ASA adherence was 74%. No statistical difference was found between a patient’s age group and medication adherence. Frequency of clinic visits showed an association with immunomodulator adherence in CD (P = 0.0150). UC patients with more severity at diagnosis had greater adherence to their medications (P = 0.005), specifically 5ASA (P = 0.0016). Urban patients with UC, when compared to rural patients, were also more likely to adhere to their 5ASA (P = 0.0776). Adherent UC patients, taking > 80% of their immunomodulators or 5ASA were more likely to be in steroid free remission in the last 6 months prior to the chart review (P = 0.015). Overall blood work adherence was 63%. Clinic visit frequency appears to significantly impact a patient’s adherence to immunomodulators in CD. Whether an optimal frequency of clinic visits exists that influence medication and blood work adherence still needs to be established with a larger cohort. Rural UC patients are more likely to be non-adherent to 5ASA. Adherent UC patients are more likely to be in sustained steroid-free remission. More support is needed for the UC rural patients to improve adherence. Qualitative research is needed to discover factors that truly motivate patients’ adherence to medications and blood work.

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)0.005
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.374
Teacher spread0.346 · 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
Published2014
Admission routes1
Has abstractyes

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