Bibliographic record
Abstract
Although double-blind studies show that lactose-intolerant individuals can consume moderate quantities of milk products without perceptible symptoms, many who perceive that they are lactose intolerant limit or avoid milk products, potentially compromising calcium and vitamin D intakes. Adult Canadians are at risk of inadequate intakes of these nutrients, but no data exist on the prevalence, correlates, and potential impact of perceived lactose intolerance among Canadians. To address this, a Web-based survey of a population-representative sample of 2251 Canadians aged ≥19 years was conducted. Overall, 16% self-reported lactose intolerance. This was more common in women (odds ratio (OR), 1.84; 95% CI, 1.46-2.33) and in nonwhites (OR, 1.79; 95% CI, 1.24-2.58) and less common in those >50 years of age (OR, 0.71; 95% CI, 0.56-0.90) and in those completing the survey in French (OR, 0.74; 95% CI, 0.56-0.99). Those with self-reported lactose intolerance had lower covariate-adjusted milk product and alternative intakes (mean ± SE; 1.40 ± 0.08 servings·day(-1) vs. 2.33 ± 0.03 servings·day(-1), p < 0.001). A greater proportion used supplements containing calcium (52% vs. 37%, p < 0.001) and vitamin D (58% vs. 46%, p < 0.001), but calcium intakes from the combination of milk products, alternatives, and supplements were lower (739 ± 30 mg·day(-1) vs. 893 ± 13 mg·day(-1), p < 0.0001). Variation in self-reported lactose intolerance by sex, age, and language preference was unexpected and suggests that some groups may be more vulnerable to the perception that they are lactose intolerant. Regardless of whether lactose intolerance is physiologically based or perceptual, education is required to ensure that calcium intakes are not compromised.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".