Vitamin D recommendations in nutritional guidelines: Protocol for a systematic review, quality evaluation using AGREE-2 and analysis of potential predictors
Bibliographic record
Abstract
Abstract Background Vitamin D has been widely promoted for bone health through supplementation and fortification of the general population. However, there is growing evidence that does not support these strategies. Our aim is to review the quality and recommendations on vitamin D nutritional and clinical practice guidelines and explore predictive factors for their direction and strength. Methods and analysis We searched PubMed, EMBASE and CINAHL databases for vitamin D guidelines for the last 10 years. We aim to perform descriptive analysis, a quality appraisal using AGREE II scores (Appraisal of Guidelines Research and Evaluation) and a bivariate analysis evaluating the association recommendations and AGREE II domains’ scores and pre-specified characteristics. Ethics and dissemination This is a systematic review protocol and therefore formal ethical approval is not required, as no primary, identifiable, personal data will be collected. Patients or the public were not involved in the design of our research. However, the findings from this review will be shared with key stakeholders, including patient groups, clinicians and guideline developers. We intend to publish our results in a suitable, peer-reviewed journal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.116 | 0.174 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.019 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.009 |
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".