TREND SHIFT IN VITAMIN-D DEFICIENCY AND SUPPLEMENTS IN LAST DECADE IN INDIA
Bibliographic record
Abstract
Vitamin-D deficiency has been noted very frequent nutritional deprivation in India as well as globally.[1] Vitamin-D synthesized in our body via skin and liver following sunshine exposure. Foods containing vitamin d are also natural source while exogenous supplementation is popular in current scenario. 25-hydroxyvitamin D (25(OH)D) is predominant metabolite of vitamin-D and is recorded as vigorous and reliable marker of vitamin D status.[2,3] Serum vitamin-D level in form of 25-OH vitamin d is easily available in pathological labs over two decades. Plenty of studies are published over 2 decades measuring 25(OH) vitamin d level and estimating prevalence of vitamin d deficiency in India.[4-13] Most of the studies are hospital based and few are from community. Vitamin-D deficiency is widely reported from every region of India despite having sunlight throughout the year. It could be attributed to several factors like darkening of skin, increased pigmentation and covered clothing habits in few communities and lack of sufficient dietary intake of vitamin-D. After availability of serum vitamin-D assay, it become popular among health care providers to check serum levels and supplementation of vitamin-D in the form of injections, capsules and syrup forms. It is noted that vitamin-D deficiency trend is improving in studies from from the USA (2007–2017), Ireland (1993–2013), Norway (1994–2008), and Canada (over 10 years of follow‑up).[14-17] Seasonal variability of serum vitamin-D should also be addressed because it also affects overall vitamin-D level as well as health status of person. Few of the studies also drawn attention towards hypervitaminosis-D. and this is also reported increasingly over last decade.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".