P255 - Anxiety and depression symptoms in Crohn's disease patients in remission
Bibliographic record
Abstract
G A A b st ra ct s (1991-1995) as our true negative population (n=936,514).We linked to health administrative data and compared the accuracy of various algorithms of health services patterns to establish which best identified children with IBD. To validate accuracy for children <18y seen throughout Ontario, the charts of 593 patients with IBD (diagnosed 2001-2005) and 1241 patients without IBD were reviewed from 12 diverse practices throughout the province. RESULTS: A two-step algorithm based on whether patients underwent diagnostic colonoscopy was most accurate. Patients who underwent colonoscopy required 4 physician contacts or 2 hospitalizations (with ICD codes for IBD) within 3 years, while those without colonoscopy required 7 contacts or 3 hospitalizations within 3 years. For patients <12 years old, this algorithm achieved sensitivity 92.5% (95%CI 86.7-96.0%), specificity 100% (95%CI 100100%), PPV 74.7% (95%CI 67.6-80.8%), NPV 100% (95%CI 100-100%). For patients <15 years, sensitivity was 89.6% (95%CI 84.0-93.5%), specificity 100% (95%CI 100-100%), PPV 57.1% (95%CI 51.2-62.9%), NPV 100% (95%CI 100-100%). Chart validation resulted in sensitivity 91.1% (95%CI 88.4-93.2%), specificity 99.5% (95%CI 98.9-99.8%), LR+ 188, LR0.090. CONCLUSION: Health administrative data can accurately identify children with IBD. This algorithm will be used to develop the Ontario Crohn's & Colitis Cohort (OCCC) in order to monitor incidence, outcomes and health services utilization of children with IBD in Ontario. Test characteristics for best two-step algorithm (scoped: 4 contacts or 2 hosp, not scoped: 7 contacts or 3 hosp) for different age groups applying cut-offs of various durations.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".