Administration of Parenteral Vitamin C in Patients With Severe Infection: Protocol for a Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Severe infections are characterized by inflammation and oxidative damage. Ascorbic acid (vitamin C) administration may attenuate oxidative damage and, in turn, reduce vascular endothelial injury in pulmonary and systemic vasculature. OBJECTIVE: We aim to describe a protocol for a living systematic review that will evaluate the effectiveness and safety of parenteral vitamin C administration in adults with severe infections, including those with COVID-19. METHODS: We searched Ovid MEDLINE, Embase, CINAHL, the Centers for Disease Control and Prevention COVID-19 database, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to March 30, 2021, for randomized controlled trials evaluating parenteral vitamin C versus no parenteral vitamin C in hospitalized adults with severe infection. Eligible studies will include at least 1 arm involving any dose of parenteral vitamin C alone or in combination with other cointerventions and at least 1 arm not involving parenteral vitamin C. The primary outcomes of interest will include in-hospital, 30-day, and 90-day mortality. Title and abstract screening, full-text screening, data extraction, and risk of bias evaluation via a modified Risk of Bias 2.0 tool will be conducted independently and in pairs. We will perform random effects modeling for meta-analyses, in which study weights will be generated by using the inverse variance method. We will assess certainty in effect estimates by using the Grading of Recommendations Assessment, Development and Evaluation methodology. Meta-analyses will be updated iteratively as new trial evidence becomes available. RESULTS: Among the 1386 citations identified as of March 30, 2021, a total of 17 eligible randomized controlled trials have been identified as of September 2021. We are in the process of updating the search strategy and associated data analyses. CONCLUSIONS: The results will be of importance to critical care physicians and hospitalists who manage severe infection and COVID-19 in daily practice, and they may directly inform international clinical guidance. Although our systematic review will incorporate the most recent trial evidence, ongoing trials may change our confidence in the estimates of effects, thereby necessitating iterative updates in the form of a living review. TRIAL REGISTRATION: PROSPERO CRD42020209187; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=209187. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/33989.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".