Vitamin C to Prevent Complex Regional Pain Syndrome in Patients With Distal Radius Fractures
Bibliographic record
Abstract
OBJECTIVE: To determine whether vitamin C is effective in preventing complex regional pain syndrome (CRPS) in patients with distal radius fractures. DATA SOURCES: MEDLINE (1946 to present), EMBASE (1974 to present), and The Cochrane Library (no date limit) were systematically searched up to September 6, 2014, using MeSH and EMTREE headings with free text combinations. STUDY SELECTION: Randomized trials comparing vitamin C against placebo were included. No exclusions were made during the selection of eligible trials on the basis of patient age, sex, fracture severity, or fracture treatment. DATA EXTRACTION: Two reviewers independently screened articles, extracted data, and applied the Cochrane Risk of Bias tool. Evidence was graded using the Grading of Recommendations Assessment, Development, and Evaluation approach. DATA SYNTHESIS: Heterogeneity was quantified using the χ test and the I statistic. Outcome data were combined with a random effects model. RESULTS: Across 3 trials (n = 890) of patients with distal radius fractures, vitamin C did not reduce the risk for CRPS (risk ratio = 0.45; 95% confidence interval, 0.18-1.13; I = 70%). This result was confirmed in sensitivity analyses to test the importance of missing data because of losses to follow-up under varying assumptions. Heterogeneity was explained by diagnostic criteria, but not regimen of vitamin C or fracture treatment. CONCLUSIONS: The evidence for vitamin C to prevent CRPS in patients with distal radius fractures fails to demonstrate a significant benefit. The overall quality of the evidence is low, and these results should be interpreted in the context of clinical expertise and patient preferences.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".