Parenteral Vitamin C in Patients with Severe Infection: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Inflammation and oxidative damage caused by severe infections may be attenuated by vitamin C. METHODS: We conducted a systematic review of randomized controlled trials (RCTs) of parenteral vitamin C as combined therapy or monotherapy versus no parenteral vitamin C administered to adults hospitalized with severe infection. The primary outcome was mortality. We performed random-effects meta-analyses and assessed certainty in effect estimates. RESULTS: Of 1547 citations, 41 RCTs (n = 4915 patients) were eligible for inclusion. Low-certainty evidence suggested that vitamin C may reduce in-hospital mortality (21 RCTs, 2762 patients; risk ratio, 0.88 [95% confidence interval (CI), 0.73 to 1.06]), 30-day mortality (24 RCTs, 3436 patients; risk ratio, 0.83 [95% CI, 0.71 to 0.98]), and early mortality (before hospital discharge or 30 days; 34 RCTs, 4366 patients; risk ratio, 0.80 [95% CI, 0.68 to 0.93]). Effects were attenuated in sensitivity analyses limited to published blinded trials at low risk-of-bias (in-hospital mortality: risk ratio, 1.07 [95% CI, 0.92 to 1.24], moderate certainty; 30-day mortality: risk ratio, 0.88 [95% CI, 0.71 to 1.10], low certainty; and early mortality: risk ratio, 0.88 [95% CI, 0.73 to 1.06], low certainty). For 90-day mortality, all trials had low risk-of-bias; moderate-certainty evidence suggested harm (five RCTs, 1722 patients; risk ratio, 1.07 [95% CI, 0.94 to 1.21]). Moderate-certainty evidence suggested an increased risk of hypoglycemia (risk ratio, 1.20 [95% CI, 0.69 to 2.08]). Effects on other secondary outcomes were mixed and informed by low-certainty evidence. No credible subgroup effects were observed for mortality related to cointerventions (monotherapy vs. combined therapy), dose, or type of infection (Covid-19 vs. other). CONCLUSIONS: Overall, evidence from RCTs does not establish a survival benefit for vitamin C in patients with severe infection. (PROSPERO number, CRD42020209187.)
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".