Development and validation of novel algorithms to identify patients with inflammatory bowel diseases in Israel: an epi-IIRN group study
Bibliographic record
Abstract
Mira Y Friedman,1,2 Maya Leventer-Roberts,3 Joseph Rosenblum,4 Nir Zigman,4 Iris Goren,4 Vered Mourad,4 Natan Lederman,5 Nurit Cohen,5 Eran Matz,6 Doron Z Dushnitzky,6 Nirit Borovsky,6 Moshe B Hoshen,3 Gili Focht,1 Malka Avitzour,1 Yael Shachar,1 Yehuda Chowers,7 Rami Eliakim,8 Shomron Ben-Horin,8 Shmuel Odes,9 Doron Schwartz,9 Iris Dotan,10 Eran Israeli,11 Zohar Levi,10 Eric I Benchimol,12–14 Ran D Balicer,3 Dan Turner1 On behalf of the Israeli IBD Research Nucleus (IIRN) 1The Juliet Keidan Institute of Pediatric Gastroenterology and Nutrition, Shaare Zedek Medical Center, The Hebrew University of Jerusalem, Jerusalem, Israel; 2Braun School of Public and Community Medicine, The Hebrew University – Hadassah Medical Center, Jerusalem, Israel; 3Clalit Research Institute, Chief’s Office, Clalit Health Services, Tel Aviv, Israel; 4Maccabi Healthcare Services, Tel Aviv, Israel; 5Meuhedet Health Services, Tel Aviv, Israel; 6Leumit Health Services, Tel Aviv, Israel; 7Department of Gastroenterology, Rambam Health Care Campus, Bruce Rappaport School of Medicine, Technion Israel Institute of Technology, Haifa, Israel; 8Department of Gastroenterology, Chaim Sheba Medical Center, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; 9Department of Gastroenterology and Hepatology, Soroka Medical Center, Ben-Gurion University of the Negev, Beer Sheva, Israel; 10Division of Gastroenterology, Rabin Medical Center, Petah Tikva, Israel; 11Institute of Gastroenterology and Liver Diseases, Hadassah Medical Center, Hebrew University, Jerusalem, Israel; 12CHEO Inflammatory Bowel Disease Centre, Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada; 13Department of Pediatrics and School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, ON, Canada; 14Institute for Clinical Evaluative Sciences, Ottawa, ON, Canada Background: Before embarking on administrative research, validated case ascertainment algorithms must be developed. We aimed at developing algorithms for identifying inflammatory bowel disease (IBD) patients, date of disease onset, and IBD type (Crohn’s disease [CD] vs ulcerative colitis [UC]) in the databases of the four Israeli Health Maintenance Organizations (HMOs) covering 98% of the population. Methods: Algorithms were developed on 5,131 IBD patients and 2,072 controls, following independent chart review (60% CD and 39% UC). We reviewed 942 different combinations of clinical parameters aided by mathematical modeling. The algorithms were validated on an independent cohort of 160,000 random subjects. Results: The combination of the following variables achieved the highest diagnostic accuracy: IBD-related codes, alone if more than five to six codes or combined with purchases of IBD-related medications (at least three purchases or ≥3 months from the first to last purchase) (sensitivity 89%, specificity 99%, positive predictive value [PPV] 92%, negative predictive value [NPV] 99%). A look-back period of 2–5 years (depending on the HMO) without IBD-related codes or medications best determined the date of diagnosis (sensitivity 83%, specificity 68%, PPV 82%, NPV 70%). IBD type was determined by the majority of CD/UC codes of the three recent contacts or the most recent when less than three contacts were recorded (sensitivity 92%, specificity 97%, PPV 97%, NPV 92%). Applying these algorithms, a total of 38,291 IBD patients were residing in Israel, corresponding to a prevalence rate of 459/100,000 (0.46%). Conclusion: The application of the validated algorithms to Israel’s administrative databases will now create a large and accurate ongoing population-based cohort of IBD patients for future administrative studies. Keywords: inflammatory bowel diseases, Crohn’s disease, ulcerative colitis, search algorithms, validation, case ascertainment, Israel, administrative database research
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".