Characterization of lBD and lBD Diagnostic Codes in the Elderly
Bibliographic record
Abstract
Background: It is important to validate the use of inflammatory bowel disease (IBD) international classification of diseases (ICD) codes in the hospital discharge abstracts, specifically for the elderly as other colonic diseases (e.g. ischemic colitis) are more common in the elderly and could lead to miscoding. Methods: From the six hospitals in Winnipeg, elderly (>65 years) and young (<50 years) patients discharged with a diagnosis of IBD between April 1, 2007 and March 31, 2010 and a random selection of elderly patients with other colonic conditions were identified and their hospital charts reviewed. Results: 170 elderly and 85 young patients with IBD discharge diagnosis and 135 elderly with other gastrointestinal discharge diagnosis were included. For Crohn's disease (CD), single ICD codes had sensitivity of 98%, specificity 96% and a positive predictive value (PPV) of 94%; for ulcerative colitis (UC) 100%, 86% and 70% respectively. Ileocolonic disease was more common in younger CD patients (58.3%) while elderly patients were more likely to have ileal or colonic disease (p<0.05). The elderly IBD were more likely to be on 5-aminosalicylates prior to hospitalization (61% vs. 43%, p=0.041) while the young IBD were more likely to be prescribed biologics (21% vs. 6%, p=0.016) and immunomodulators (42% vs 21%, p=0.01 ). Conclusions: A single ICD-10 IBD code is satisfactory to identify elderly CD hospitalized patients, and although less accurate, still has good specificity to identify elderly UC patients. Biologicals and immunomodulators are used less often in the elderly IBD, even among those with disease severe enough to require hospitalization.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".