Derivation and validation of a diagnostic test for irritable bowel syndrome using latent class analysis
Bibliographic record
Abstract
BACKGROUND: The accuracy of symptom-based diagnostic criteria for irritable bowel syndrome (IBS) is modest. AIMS: To derive and validate a new test that utilises latent class analysis. METHODS: Symptom, colonoscopy, and histology data were collected from 1981 patients and 360 patients in two cohorts referred to secondary care for investigation of their gastrointestinal symptoms in Canada and the UK, respectively. Latent class analysis was used to identify naturally occurring clusters in patient-reported symptoms in the Canadian dataset, and the latent class model derived from this was then applied to the UK dataset in order to validate it. Sensitivity, specificity, and positive and negative likelihood ratios (LRs) were calculated for the latent class models. RESULTS: In the Canadian cohort, the model had a sensitivity of 44.7% (95% CI 40.0-50.0) and a specificity of 85.3% (95% CI 83.4-87.0). Positive and negative LRs were 3.03 (95% CI 2.57-3.56) and 0.65 (95% CI 0.59-0.71) respectively. A maximum positive LR of 3.93 was achieved following construction of a receiver operating characteristic curve. The performance in the UK cohort was similar, with a sensitivity and specificity of 52.5% (95% CI 42.2-62.7) and 84.3% (95% CI 79.3-88.6), respectively. Positive and negative LRs were 3.35 (95% CI 2.38-4.70) and 0.56 (95% CI 0.45-0.68), respectively, with a maximum positive LR of 4.15. CONCLUSIONS: A diagnostic test for IBS, utilising patient-reported symptoms incorporated into a latent class model, performs as accurately as symptom-based criteria. It has potential for improvement via addition of clinical markers, such as coeliac serology and faecal calprotectin.
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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 | 0.025 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".