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Record W2911459233 · doi:10.1097/ccm.0000000000003262

Corticosteroids in Sepsis: An Updated Systematic Review and Meta-Analysis

2018· review· en· W2911459233 on OpenAlexaff
Bram Rochwerg, Simon Oczkowski, Reed Siemieniuk, Thomas Agoritsas, Emilie P. Belley‐Côté, Frédérick D’Aragon, Erick Duan, Shane English, Kira Gossack‐Keenan, Mashari Alghuroba, Wojciech Szczeklik, Kusum Menon, Waleed Alhazzani, Jonathan Sevransky, Per Olav Vandvik, Djillali Annane, Gordon Guyatt

Bibliographic record

VenueCritical Care Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsUniversity of OttawaCentre Hospitalier Universitaire de SherbrookeMcMaster UniversityOttawa HospitalImpact
Fundersnot available
KeywordsMedicineRelative riskRandomized controlled trialMeta-analysisData extractionMEDLINESubgroup analysisStudy heterogeneityAbsolute risk reductionPlaceboIntensive care medicineSepsisGuidelinePublication biasInternal medicineConfidence intervalAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: This systematic review and meta-analysis addresses the efficacy and safety of corticosteroids in critically ill patients with sepsis. DATA SOURCES: We updated a comprehensive search of MEDLINE, EMBASE, CENTRAL, and LILACS, and unpublished sources for randomized controlled trials that compared any corticosteroid to placebo or no corticosteroid in critically ill children and adults with sepsis. STUDY SELECTION: Reviewers conducted duplicate screening of citations, data abstraction, and, using a modified Cochrane risk of bias tool, individual study risk of bias assessment. DATA EXTRACTION: A parallel guideline committee provided input on the design and interpretation of the systematic review, including the selection of outcomes important to patients. We assessed overall certainty in evidence using Grading of Recommendations Assessment, Development and Evaluation methodology and performed all analyses using random-effect models. For subgroup analyses, we performed metaregression and considered p value less than 0.05 as significant. DATA SYNTHESIS: Forty-two randomized controlled trials including 10,194 patients proved eligible. Based on low certainty, corticosteroids may achieve a small reduction or no reduction in the relative risk of dying in the short-term (28-31 d) (relative risk, 0.93; 95% CI, 0.84-1.03; 1.8% absolute risk reduction; 95% CI, 4.1% reduction to 0.8% increase), and possibly achieve a small effect on long-term mortality (60 d to 1 yr) based on moderate certainty (relative risk, 0.94; 95% CI, 0.89-1.00; 2.2% absolute risk reduction; 95% CI, 4.1% reduction to no effect). Corticosteroids probably result in small reductions in length of stay in ICU (mean difference, -0.73 d; 95% CI, -1.78 to 0.31) and hospital (mean difference, -0.73 d; 95% CI, -2.06 to 0.60) (moderate certainty). Corticosteroids result in higher rates of shock reversal at day 7 (relative risk, 1.26; 95% CI, 1.12-1.42) and lower Sequential Organ Failure Assessment scores at day 7 (mean difference, -1.39; 95% CI, -1.88 to -0.89) (high certainty). Corticosteroids likely increase the risk of hypernatremia (relative risk, 1.64; 95% CI, 1.32-2.03) and hyperglycemia (relative risk, 1.16; 95% CI, 1.08-1.24) (moderate certainty), may increase the risk of neuromuscular weakness (relative risk, 1.21; 95% CI, 1.01-1.52) (low certainty), and appear to have no other adverse effects (low or very low certainty). Subgroup analysis did not demonstrate a credible subgroup effect on any of the outcomes of interest (p > 0.05 for all). CONCLUSIONS: In critically ill patients with sepsis, corticosteroids possibly result in a small reduction in mortality while also possibly increasing the risk of neuromuscular weakness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.024
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.049
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0240.040
Bibliometrics0.0150.012
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.121
GPT teacher head0.437
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations258
Published2018
Admission routes1
Has abstractyes

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