Biomarkers for bile acid diarrhoea in functional bowel disorder with diarrhoea: a systematic review and meta-analysis
Bibliographic record
Abstract
There is no universally available laboratory test to diagnose bile acid diarrhoea (BAD). Objective To conduct a systematic review and meta-analysis to identify a biomarker for idiopathic BAD in patients with functional bowel disorder (FBD) with diarrhoea. Design We searched multiple databases through 15 May 2015. Data were only available to estimate the diagnostic yield of each test (the prevalence of a positive test). Estimates were pooled across studies using the random effects model. Results We included 36 studies, enrolling 5028 patients (24 using 75 selenium homotaurocholic acid test ( 75 SeHCAT) retention of <10%, 6 using fasting serum C4, 3 using fasting serum fibroblast growth factor 19 (FGF19) and 2 based on total faecal bile acid (BA) excretion over 48 h). The diagnostic yields (and 95% CI) of abnormal tests were: 0.308 (0.247 to 0.377) for 75 SeHCAT retention (<10%), 0.171 (0.134 to 0.217) for serum C4, 0.248 (0.147 to 0.385) for serum FGF19 and 0.255 (0.071 to 0.606) for total faecal BA excretion over 48 h. The majority of the analyses were associated with substantial heterogeneity. Performance characteristics relative to a gold standard test could not be estimated. Conclusions Overall, the test with the highest diagnostic yield conducted in the largest number of studies was 75 SeHCAT retention, which is not widely available in many countries outside Europe and Canada. Using different diagnostic tests, 25% (average) of patients with lower FBD with diarrhoea has evidence of idiopathic BAD. These tests serve to identify idiopathic BAD among patients with FBD with diarrhoea. Further studies are required to appraise the performance characteristics of tests for idiopathic BAD.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".