Paediatric inflammatory bowel disease: improving early diagnosis
Bibliographic record
Abstract
For many conditions, delayed diagnosis results in worse outcomes, increased mortality and amplified disease burden. In children, an emphasis on rapid, accurate diagnosis in leukaemia, lymphoma and solid tumours has been associated with an increasing survival rate and reduced morbidity over the last 25 years. The challenge is to extend early diagnosis to chronic conditions in children where early intervention will improve long-term outcomes. Similarly in adult patients, rapid access clinics for specific conditions are now routine, enabling quick referral to the specialist care service to make the precise diagnosis and start the correct treatment. In their paper, Riccuito et al reported diagnostic delay and subsequent impact on outcome in Canadian children with inflammatory bowel disease (IBD).1 IBD, consisting of Crohn’s disease, ulcerative colitis and IBD unclassified, is a chronic, heterogeneous, relapsing and remitting condition primarily as a consequence of inflammation within the bowel lumen. Early and effective treatment is crucial to control symptoms, minimise impact on nutrition and growth and enable the child to function well (eg, attend school). Over the last 20 years, there has been a steady increase in the incidence of paedaitric inflammatory bowel disease (PIBD), with a consequent increase in children presenting to primary, secondary and tertiary care.2 Paediatric gastroenterologists have been concerned for over 25 years that diagnostic delay is common. This delay is clearly multifactorial, and to determine how best to effect change would require an analysis of all aspects of the patient pathway. Riccuito …
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".