Vitamin A deficiency: policy implications of estimates of trends and mortality in children – Authors' reply
Bibliographic record
Abstract
Our reference1Stevens GA Bennett JE Hennocq Q et al.Trends and mortality effects of vitamin A deficiency in children in 138 low-income and middle-income countries between 1991 and 2013: a pooled analysis of population-based surveys.Lancet Glob Health. 2015; 3: e528-e536Summary Full Text Full Text PDF PubMed Scopus (286) Google Scholar to the work of Mason and colleagues2Mason J Greiner T Shrimpton R Sanders D Yukich J Vitamin A policies need rethinking.Int J Epidemiol. 2015; 44: 283-292Crossref PubMed Scopus (76) Google Scholar was in a sentence that stated that “until this [an improvement in the overall nutrition of the population, including through dietary diversification and improved access to vitamin-rich foods, and improved access to treatment for infectious diseases] happens, large scale or targeted supplementation or fortification strategies will probably have some beneficial effect on mortality in these [south Asia and sub-Saharan Africa] regions”. This approach, whose ultimate aim is regular intake, largely through diet, is not inconsistent with the prudent phase-over approach outlined by Mason and colleagues.2Mason J Greiner T Shrimpton R Sanders D Yukich J Vitamin A policies need rethinking.Int J Epidemiol. 2015; 44: 283-292Crossref PubMed Scopus (76) Google Scholar We declare no competing interests. Vitamin A deficiency: policy implications of estimates of trends and mortality in childrenWe welcome the Article by Gretchen Stevens and colleagues (September, 2015),1 although wish to correct a, no doubt, inadvertent misrepresentation of our views. Stevens and colleagues cite our paper2 in support of their statement that “large scale or targeted supplementation or fortification strategies will probably have some beneficial effect on mortality in these regions [south Asia and sub-Saharan Africa]”. We advocated a prudent phase-over away from vitamin A capsules to more physiological and locally appropriate methods. Full-Text PDF Open Access
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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 | 0.018 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.029 | 0.055 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".