Is it time to include older adults in inflammatory bowel disease trials? A call for action
Bibliographic record
Abstract
The therapeutic management of older patients with inflammatory bowel disease (IBD) is challenging, particularly because of the absence of evidence-based guidelines for these patients, who seem to frequently be excluded from clinical trials. In this systematic review we investigated the exclusion of older patients with IBD from phase 3 studies registered on PubMed and ClinicalTrials.gov, by assessing the upper limit of age exclusion criteria and the percentage of patients older than 65 years included in the trials. Exclusion criteria other than age were also recorded, and comorbidities were analysed separately. Our review of 222 phase 3 studies shows that older patients are frequently excluded from IBD clinical trials because of their age, which was used as an exclusion criterion in 129 (58%) of the 222 assessed trials. Of the 32 trials that detailed the percentage of included patients who were 65 years or older, only 763 (5·4%) patients of the 14 124 patients included were older than 65 years. In addition to age, patients were also excluded because of comorbidities (mainly renal, hepatic, and cardiovascular, and used as an exclusion criterion in 76% of trials), a history of dysplasia (45% of trials), and previous treatment for IBD (19% of trials). We propose a three-step process that should enable the inclusion of all older patients in IBD clinical trials, regardless of their age, comorbidities, and frailty.
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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 | 0.415 | 0.488 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.037 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".