Therapeutic preferences and outcomes in newly diagnosed patients with Crohn’s diseases in the biological era in Hungary: a nationwide study based on the National Health Insurance Fund database
Bibliographic record
Abstract
BACKGROUND: Accelerated treatment strategy, including tight disease control and early aggressive therapy with immunosuppressives (IS) and biological agents have become increasingly common in inflammatory bowel disease (IBD). The aim of the present study was to estimate the early treatment strategy and outcomes in newly diagnosed patients with Crohn's disease (CD) between 2004 and 2008 and 2009-2015 in the whole IBD population in Hungary based on the administrative database of the National Health Insurance Fund (OEP). METHODS: We used the administrative database of the OEP, the only nationwide state-owned health insurance provider in Hungary. Patients were identified through previously reported algorithms using the ICD-10 codes for CD in the out-, inpatient (medical, surgical) non-primary care records and drug prescription databases between 2004 and 2015. Patients were stratified according to the year of diagnosis and maximum treatment steps during the first 3 years after diagnosis. RESULTS: A total of 6173 (male/female: 46.12%/53.87%) newly diagnosed CD patients with physician-diagnosed IBD were found in the period of 2004-2015. The use of 5-ASA and steroids remained common in the biological era, while immunosuppressives and biologicals were started earlier and became more frequent among patients diagnosed after 2009. The probability of biological therapy was 2.9%/6.4% and 8.4%/13.7% after 1 and 3 years in patients diagnosed in 2004-2008/2009-2015. The probability of hospitalization in the first 3 years after diagnosis was different before and after 2009, according to the maximal treatment step (overall 55.7%vs. 47.4% (p = 0.001), anti-TNF: 73%vs. 66.7% (p = 0.103), IS: 64.6% vs. 56.1% (p = 0.001), steroid: 44.2%vs. 36.8% (p < 0.007), 5-ASA: 32.6% vs. 26.7% p = 0.157)). In contrast, surgery rates were not significantly different in patients diagnosed before and after 2009 according to the maximum treatment step (overall 16.0%vs.15.3%(p = 0.672) anti-TNF 26.7%vs.27.2% (p = 0.993), IS: 24.1%vs22.2% (p = 0.565), steroid 8.1%vs.7.9% (p = 0.896), 5-ASA 10%vs. 11% (p = 0.816)). CONCLUSIONS: IS and biological exposure became more frequent, while hospitalization decreased and surgery remained low but constant during the observation period. Use of steroids and 5-ASA remained high after 2009. The association between the maximal treatment step and hospitalization/surgery rates suggests that maximal treatment step can be regarded as proxy severity marker in patients with IBD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".