Studies based on health administrative data regarding rare outcomes in inflammatory bowel disease significantly underestimate the true risk—the importance of specificity
Bibliographic record
Abstract
Health administrative data (HAD) have significantly increased our knowledge of rare outcomes in inflammatory bowel disease (IBD), such as cancer and mortality. We aimed to assess the information bias imposed by misclassification of the IBD diagnosis in HAD studies by performing quantitative bias analysis (QBA). In a narrative review, we identified pediatric-onset IBD (PIBD) HAD studies assessing cancer risk in which the PIBD case identification was based on published validation studies. We then performed QBA to adjust for non-differential exposure misclassification using the sensitivity and specificity values from country or region-specific validation studies. We present QBA on four recent studies reporting on cancer outcomes. Generally, we found the reported cancer risks biased towards the null. In the most extreme example, the relative risk changed from 2.0 (95% CI, 1.2-3.4) to 5.8 (95%CI, 2.5-13.7) after bias adjustment. The risk difference for this example rose from 1.0% (95% CI, 0.1-1.9) to 3.8% (95%CI, 1.4-7.9) after bias adjustment. The results from this study indicate that most HAD-based studies on rare long-term consequences of IBD significantly underestimate the true risk of the outcomes. These results can be extrapolated to other HAD-based studies with imperfect specificity of the case assertion algorithms.
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 | 0.284 | 0.538 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".