IV Vitamin C in Critically Ill Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To conduct a systematic review and meta-analysis to evaluate the impact of IV vitamin C on outcomes in critically ill patients. DATA SOURCES: Systematic search of MEDLINE, EMBASE, CINAHL, and the Cochrane Register of Controlled Trials. STUDY SELECTION: Randomized controlled trials testing IV vitamin C in critically ill patients. Data Abstraction: Two independent reviewers abstracted patient characteristics, treatment details, and clinical outcomes. DATA SYNTHESIS: Fifteen studies involving 2,490 patients were identified. Compared with placebo, IV vitamin C administration is associated with a trend toward reduced overall mortality (relative risk, 0.87; 95% CI, 0.75–1.00; p = 0.06; test for heterogeneity I 2 = 6%). High-dose IV vitamin C was associated with a significant reduction in overall mortality (relative risk, 0.70; 95% CI, 0.52–0.96; p = 0.03), whereas low-dose IV vitamin C had no effect (relative risk, 0.94; 95% CI, 0.79–1.07; p = 0.46; test for subgroup differences, p = 0.14). IV vitamin C monotherapy was associated with a significant reduction in overall mortality (relative risk, 0.64; 95% CI, 0.49–0.83; p = 0.006), whereas there was no effect with IV vitamin C combined therapy. No trial reported an increase in adverse events related to IV vitamin C. CONCLUSIONS: IV vitamin C administration appears safe and may be associated with a trend toward reduction in overall mortality. High-dose IV vitamin C monotherapy may be associated with improved overall mortality, and further randomized controlled trials 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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".