Diagnostic misclassification reduces the ability to detect linkage in inflammatory bowel disease genetic studies
Bibliographic record
Abstract
BACKGROUND: Linkage data have now identified several inflammatory bowel disease (IBD) susceptibility loci but these data have not been consistently replicated in independent studies. One potential explanation for this is the possibility that patients enrolled in such studies may have been erroneously classified with respect to their diagnosis. AIMS: To determine the rate and type of misclassification in a large population of individuals referred for participation in an IBD genetics study and to examine the effect of diagnostic misclassification on the power to detect linkage. METHODS: The medical records of 1096 patients entered into an IBD genetics programme were reviewed using standardised diagnostic criteria. The original patient reported diagnoses were changed, if necessary, based on review, and the reasons for the change in diagnosis were recorded. To evaluate the effect of misclassification on linkage results, simulations were created with Gensim and analysed using Genehunter to evaluate a model for IBD inheritance. RESULTS: Sixty eight of 1096 (6.2%) individuals had a change in diagnosis from that originally reported. The majority of changes were patients with either Crohn's disease or ulcerative colitis who were determined not to have IBD at all. The principal reasons for changes to the original diagnosis were discordance between the patients' subjective reports of diagnosis and actual clinical history, endoscopic, or pathological results; a change in disease pattern over time; and insufficient information available to confirm the original diagnosis. A 10% misclassification rate resulted in 28.4% and 40.2% loss of power to detect a true linkage when using a statistical model for a presumed IBD locus with lambda(s) values of 1.8 and 1.3, respectively. CONCLUSIONS: Diagnostic misclassification occurs in patients enrolled in IBD genetic studies and frequently involves assigning the diagnosis of IBD to non-affected individuals. Even low rates of diagnostic misclassification can lead to significant loss of power to detect a true linkage, particularly for loci with modest effects as are likely to be found in 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 | 0.002 |
| 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".