Assessment of the quality and content of clinical practice guidelines (CPGs) for vitamin D and for immigrants using the AGREE-II instrument: a protocol for systematic review
Bibliographic record
Abstract
Abstract Introduction Worldwide, more immigrants experience vitamin D (vitD) deficiency than non-immigrants, which is attributed to ethnic variations, place or region of birth, skin pigmentation, clothing style, and resettlement-related changes in diet, physical activity, and sun exposure. Current recommendations in clinical practice guidelines (CPGs) concerning vitD are inadequate to address vitD deficiency among immigrants. CPGs may also lack guidance for physicians on vitD supplementation for immigrants. Moreover, there are concerns about the overall quality of these CPGs. Objectives This systematic review will collate and critically appraise CPGs relevant to immigrants’ health and vitD. Moreover, we will evaluate whether the CPGs of vitD including recommendations for immigrants and clarify whether the CPGs of immigrants include recommendations on vitD. Methods A systematic search of Ovid MEDLINE® ALL, EMBASE, and Turning Research Into Practice (TRIP) electronic databases, guideline repositories, and gray literature will be conducted to identify relevant CPGs. Two reviewers will independently evaluate the methodological quality of the retrieved guidelines using the Appraisal of Guidelines, Research, and Evaluation-II (AGREE-II) instrument. CPGs scoring ≥60% in at least four domains, including “rigor of development,” will be considered high quality. Conclusion Evaluating the quality and content of relevant CPGs may support researchers in developing national and global guidelines for immigrants. Furthermore, it may support vitD testing, nutritional counseling, and supplementation for vulnerable immigrant sub-populations. Systematic review registration PROSPERO CRD42021240562.
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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 | 0.184 | 0.275 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".