A95 THE IMPACT OF AGE ON NON-COMPLIANCE IN AN AMBULATORY ACADEMIC INFLAMMATORY BOWEL DISEASE (IBD) PRACTICE
Bibliographic record
Abstract
Compliance is critical to the foundation of high quality IBD care. However, many studies rely on self-reported compliance metrics without corroboration. Pediatric to adult transition of care in IBD is a challenging period during which compliance is compromised. To determine the impact of transition age (18–25) on non-compliance. A retrospective cohort study was performed using data from a sample of gastroenterology patients (n=245) recorded in the electronic medical record between January 1, 2015 and June 30, 2017. Data was extracted between July 1st, 2017 and August 31st, 2017. Data on patient sex, age at last encounter (separated into 4 categories), age of diagnosis, residential address, disease type, comorbidities, medications, substance use, family history of IBD and markers of non-compliance were collected. Single variable logistic regression was used to determine the impact of age category on compliance. Multivariable logistic regression was used to investigate the likelihood of non-compliance depending on age category while adjusting for baseline characteristics. 245 patients were included and separated (18–25 (n=27), 26–45 (n=109), 46–65 (n=84), >65 (n=25)). 49% had Crohn’s Disease (CD), the mean age at last interaction was 43 +/-15, mean age of IBD diagnosis 31 +/-14, 54% were on biologic therapy, 53% had another non-IBD related comorbidity, 50% reported some form of substance use, 33% had a family history of IBD. The 18–25 group was more likely to not show up for a follow-up visit (33% vs 23% vs 11% vs 12%, p=0.002), not show up to a GP appointment (30% vs 29% vs 25% vs 28%, p=0.0081), arrive late to a GI appointment (56% vs 53% vs 45% vs 48%, p=0.001). They were less likely to cancel a GI appointment in advance (56% vs 66% vs 58% vs 56% p=0.002). Patients aged 18–25 were less likely to volunteer self-discontinuation or inconsistent use of medications, as documented in the physician notes (19% vs 42% vs 43% vs 44%, (p=0.002) and were more likely to have no documented non-compliance in the physician notes (59% vs 41% vs 34% vs 44%, p=0.0002). When focusing on no-show, late arrivals or cancelled visits, a multivariable logistic regression model revealed that all other age categories demonstrated less non-compliance compared to the 18–25 age group (26–45 years OR -1.50 [-2.24,-0.75], 46–65 years OR -2.57 [-3.43,-1.72], >65 years OR -2.62 [-3.61,-1.63]. However, when focusing only on physician documented non-compliance, multivariable logistic regression model did not reveal age as a significant predictor. Transition aged patients 18–25 show more objective forms of non-compliance but are less likely to volunteer their non-compliance in physician interactions, making self-reported compliance metrics unreliable. Department of Medicine, Women’s College Hospital
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".