Efficacy of weekly <i>versus</i> daily cholecalciferol for repleting serum vitamin D (25(OH)D) deficiency: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND/RATIONALE: Weekly cholecalciferol can replace daily supplementation to reduce pill burden in patients with complex medication regimens and hypovitaminosis D, but evidence supporting this switch is unclear. OBJECTIVE: We aimed to determine whether weekly cholecalciferol was superior to daily cholecalciferol to replete patients with hypovitaminosis D. METHODS: We conducted a systematic review of randomized controlled trials involving participants with baseline hypovitaminosis D (<30 ng/ml) comparing weekly versus daily cholecalciferol dosing and where serum cholecalciferol was measured within 120 days of starting treatment. We searched MEDLINE, CINAHL and EMBASE from inception to 7 May 2024. A random-effects meta-analysis evaluated the odds ratio for repletion of serum vitamin D levels. FINDINGS: = 85.3%). A sensitivity analysis excluding otherwise healthy patients had similar findings (OR = 0.8, 95% CI = 0.3-2.1, p = 0.6). Most studies were at risk of bias; the different doses being compared increased the heterogeneity. CONCLUSIONS: Limited direct evidence supports a switch from daily to weekly cholecalciferol dosing; however, weekly supplementation was not demonstrably worse at repleting levels and decreased a patient's daily pill burden.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".