Association Between Vitamin D Dosing Regimen and Fall Prevention in Long-term Care Seniors.
Bibliographic record
Abstract
INTRODUCTION: The effectiveness of vitamin D in reducing falls among long-term care (LTC) seniors remains nonconclusive. We reviewed how vitamin D dosing regimen could affect rate of fall and number of fallers among LTC seniors. METHODS: We conducted a systematic literature review. Studies were selected by two independent reviewers based on study characteristics (age 75 or older), quality assessment (primary analysis randomized controlled trials), and outcome (rate of fall and number of fallers). Analyses of all trials following trials using daily standard dosage (800-1000 IU) only were performed to compare daily standard dosage with intermittent supratherapeutic dosage in fall prevention. RESULTS: Seventy-nine studies were identified, with 28 selected by reviewers (kappa 0.98), and four RCT were conducted in LTC. Daily standard dosage provides greater reduction in rate of fall by 16%, which was statistically significant. However, reduction in number of fallers remained statistically insignificant even taking dosing regimen into account. CONCLUSIONS: Daily standard dosage of vitamin D has greater benefits in reducing fall rate than that of intermittent supratherapeutic doses, but not in number of fallers. This could imply that vitamin D is useful in preventing fall recurrence rather than first fall. Prospective studies randomizing LTC seniors to different dosing regimens are warranted.
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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 | 0.009 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".