Meta‐analysis: the diagnostic accuracy of lactose breath hydrogen or lactose tolerance tests for predicting the North European lactase polymorphism C/T‐13910
Bibliographic record
Abstract
BACKGROUND: The diagnostic accuracy of two indirect tests of lactose digestion, lactose breath hydrogen and lactose tolerance tests, have not been systematically reviewed for comparison with available publications on genotype. AIM: To perform a meta-analysis of available studies that compares the north-European genetic polymorphism C/T-13910 with the lactose breath hydrogen and the lactose tolerance tests, to determine their ability to predict geno/phenotype relationships. We examine the effects of lactose loading dose, inclusion of children and latitudes of study centre on comparative outcome. METHODS: An electronic database of the literature as well as individual references in articles were searched with the theme of genetics of lactase and comparisons with breath or lactose tolerance tests were carried out. Random effect and fixed effect models were used for breath and lactose tolerance tests respectively, to report summary accuracy measures with 95% confidence intervals (CI). RESULTS: The search revealed 19 studies: 17 evaluated breath hydrogen, five lactose tolerance test (3/17 overlapped). Overall sensitivity was 0.88 (CI, 0.85-0.90), specificity was 0.85 (CI, 0.82-0.87) for breath test. Heterogeneity was explored by adjusting for studies including children, high or low dose lactose and to some extent by site of study. The lactose tolerance test showed sensitivity of 0.94 (0.9-0.97) and specificity of 0.90 (0.84-0.95) with a nonsignificant heterogeneity. CONCLUSION: The diagnostic accuracy of both tests individually reflects expected geno/phenotypes when the populations are well defined.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.055 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".