Bibliographic record
Abstract
Much of the original work on vitamin D and asthma incidence focused on birth cohort studies. These studies examined the effect of maternal intake of vitamin D during pregnancy and its impact on asthma/wheeze in the first few years of life [1–3]. In all three of these methodologically well-performed birth cohort studies, higher maternal intake of vitamin D was associated with a lower incidence of asthma and wheeze in the child. However, one cohort study with very a small sample size and high loss to follow-up may have obtained spurious negative results as a consequence of these significant methodological flaws [4]. Another relevant concern is that the positive studies did not measure vitamin D directly but simply looked at maternal vitamin D intake, mostly from supplements, as a proxy for vitamin D levels. This brings us to the study by Hollams et al. [5] in this issue of the European Respiratory Journal , which looked at 989 6-yr-olds and 1,380 14-yr-olds from an unselected birth cohort in Perth, Australia. Of the Raine cohort participants, 689 subjects were seen longitudinally at both ages 6 and 14 yrs, and the predictive value of vitamin D levels at age 6 yrs could be used to assess clinical outcomes at age 14 yrs. Loss to follow-up, the major validity threat in a study such as this, was demonstrated not to influence their results. Vitamin D levels at ages 6 and 14 yrs were predictive of allergy/asthma outcomes at that age. But more importantly, vitamin D levels at age 6 yrs were predictive of subsequent atopy/asthma-associated phenotypes at age 14 yrs. The significant results were restricted to males. This study is the first to demonstrate such an association, as seen in the early-life birth cohort studies, …
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".