Hydrogen Breath Test for Diagnosis of Lactose Malabsorption: The Importance of Timing and the Number of Breath Samples
Bibliographic record
Abstract
BACKGROUND: The hydrogen breath test (H(2)BT) is the most widely used procedure in the diagnostic workup of lactose malabsorption and lactose intolerance. AIM: To establish whether a simplified two- or three-sample test may reduce time, costs and staff resources without reducing the sensitivity of the procedure. PATIENTS AND METHODS: Data from 1,112 patients (292 men, 820 women) with a positive 4 h, nine-sample H(2)BT were retrospectively analyzed. Patients were stratified according to the degree of lactose malabsorption, the occurrence and type of symptoms. Loss of sensitivity in the procedure was evaluated taking into account two-sample tests (0 min and 120 min or 0 min and 210 min) or three-sample tests (0 min, 120 min and 180 min or 0 min, 120 min and 210 min). RESULTS: Using a two-sample test (0 min and 120 min or 0 min and 210 min) the false-negative rate was 33.4% and 22.7%, respectively. With a three-sample test (0 min, 120 min and 180 min or 0 min, 120 min or 210 min), lactose malabsorption was diagnosed in 91.2% (1,014 of 1,112) patients and in 96.1% (1,068 of 1,112) patients, respectively. Of 594 patients with abdominal symptoms, 158 (26.6%) and 73 (12.2%) would have false-negative results with 0 min and 120 min or 0 min and 210 min two-sample tests, respectively. The three-sample tests, 0 min, 120 min and 180 min or 0 min, 120 min and 210 min, have a false-negative rate of 5.9% and 2.1%, respectively. CONCLUSIONS: A three-sample H(2)BT is time- and cost-sparing without significant loss of sensitivity for the diagnosis both of lactose malabsorption and lactose intolerance.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".