Expert consensus on vitamin D in osteoporosis
Bibliographic record
Abstract
Background: Adequate vitamin D is essential for maintaining optimal bone health, preventing and treating of osteoporosis. However, in recent years, large clinical trials and meta-analyses on the efficacy of vitamin D supplementation to prevent fractures in populations at different risks have been equivocal. The optimal level of 25-hydroxyvitamin D (25[OH]D) remains controversial. Recommendations vary between societies. The lack of standardized assays also poses a challenge in interpreting available research data. Methods: We systematically searched for articles in MEDLINE database through PubMed, which included meta-analysis, systematic reviews of randomized controlled trials (RCTs) and observational studies that assessed measurement, diagnosis and treatment about vitamin D deficiency. The experts evaluated the available literature, graded references according to the type of study and described the strength recommendations. Results: This expert consensus is based on the review of relevant clinical evidence and provides nine key recommendations on vitamin D deficiency in populations at different risks, especially in patients with osteoporosis. Supporting information is provided in the subsequent appendix box. Conclusions: This expert consensus is a practical tool for endocrinologists, general physicians for the diagnosis, assessment, and treatment of populations at different risks of vitamin D deficiency, especially in patients with osteoporosis. Clinicians should be aware of the evidence but make individualized decisions based on specific patients or situation.
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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 | 0.050 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".