Elderly patients with inflammatory bowel disease: Updated review of the therapeutic landscape
Bibliographic record
Abstract
High-quality data remains scarce in terms of optimal management strategies in the elderly inflammatory bowel disease (IBD) population. Indeed, available trials have been mostly retrospective, of small sample size, likely owing to under-representation of such a population in the major randomized controlled trials. However, in the last five years, there has been a steady increase in the number of published trials, helping clarify the estimated benefits and toxicity of the existing IBD armamentarium. In the Everhov trial, prescription strategies were recorded over an average follow-up of 4.2 years. A minority of elderly IBD patients (1%-3%) were treated with biologics within the five years following diagnosis, whilst almost a quarter of these patients were receiving corticosteroid therapy at year five of follow-up, despite its multiple toxicities. The low use of biologic agents in real-life settings likely stems from limited data suggesting lower efficacy and higher toxicity. This minireview will aim to highlight current outcome measurements as it portends the elderly IBD patient, as well as summarize the available therapeutic strategies in view of a growing body of evidence.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".