Methylene Blue in Septic Shock: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Although clinicians may use methylene blue (MB) in refractory septic shock, the effect of MB on patient-important outcomes remains uncertain. We conducted a systematic review and meta-analysis to investigate the benefits and harms of MB administration in patients with septic shock. DATA SOURCES: We searched six databases (including PubMed, Embase, and Medline) from inception to January 10, 2024. STUDY SELECTION: We included randomized clinical trials (RCTs) of critically ill adults comparing MB with placebo or usual care without MB administration. DATA EXTRACTION: Two reviewers performed screening, full-text review, and data extraction. We pooled data using a random-effects model, assessed the risk of bias using the modified Cochrane tool, and used Grading of Recommendations Assessment, Development, and Evaluation to rate certainty of effect estimates. DATA SYNTHESIS: We included six RCTs (302 patients). Compared with placebo or no MB administration, MB may reduce short-term mortality (RR [risk ratio] 0.66 [95% CI, 0.47-0.94], low certainty) and hospital length of stay (mean difference [MD] -2.1 d [95% CI, -1.4 to -2.8], low certainty). MB may also reduce duration of vasopressors (MD -31.1 hr [95% CI, -16.5 to -45.6], low certainty), and increase mean arterial pressure at 6 hours (MD 10.2 mm Hg [95% CI, 6.1-14.2], low certainty) compared with no MB administration. The effect of MB on serum methemoglobin concentration was uncertain (MD 0.9% [95% CI, -0.2% to 2.0%], very low certainty). We did not find any differences in adverse events. CONCLUSIONS: Among critically ill adults with septic shock, based on low-certainty evidence, MB may reduce short-term mortality, duration of vasopressors, and hospital length of stay, with no evidence of increased adverse events. Rigorous randomized trials evaluating the efficacy of MB in septic shock are needed. REGISTRATION: Center for Open Science (https://osf.io/hpy4j).
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".