Clinical and Demographic Characteristics of Older Inflammatory Bowel Disease Patients
Bibliographic record
Abstract
There is limited data describing management of older Australians with inflammatory bowel disease (IBD). The prevalence of IBD in the elderly is increasing due to an aging population and increasing overall incidence, which presents challenges in management due to comorbidities and polypharmacy. The aim of the study was to describe demographic and clinical characteristics, and management of IBD in older patients. Around 100 consecutive patients (n = 100) aged above 60 years and attending the IBD outpatient clinic of Fiona Stanley Hospital from January 2019 were selected. Demographic and clinical information were compiled from the digital medical record. Around 59 patients had Crohn’s disease (CD), 39 had ulcerative colitis (UC) and 2 unclassified IBD (IBD-U). Around 75% of patients had a Montreal classification recorded, which is a recommended standard of care. For both CD and UC, no patients had onset of symptoms <17 years old, and the majority had symptom onset after 40 years of age (CD—56%, UC—52%). Around 39% of CD patients had undergone at least one surgery, and 5% a second operation. No UC patients had undergone surgery. 5’Aminosalicylates (59%), antitumor necrosis factor alpha (33%), thiopurines (22%), and vedolizumab (11%) were the most common current treatments. Around 9% of patients were currently taking steroids while 36% had previously taken corticosteroids. Immunosuppressive events were recorded, if they occurred, after treatment with steroids, immunomodulatory or biologic agents. Two patients had melanoma, 12 nonmelanoma skin cancer, 3 solid organ tumors (2 prostate adenocarcinoma and 1 bladder transitional cell carcinoma), 3 latent tuberculosis, 1 myelodysplasia, and 1 septic arthritis. Around 14% of the patients had osteoporosis or osteopenia; 43% of these had prior corticosteroid exposure; however, there was a low rate of bone densitometry. Most patients both with CD and UC were diagnosed over 40. Biologics were the most common treatment category in keeping with the aim of achieving deep remission.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".