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Record W4382584158 · doi:10.1097/cce.0000000000000938

Systemic Nonsteroidal Anti-Inflammatories for Analgesia in Postoperative Critical Care Patients: A Systematic Review and Meta-Analysis of Randomized Control Trials

2023· review· en· W4382584158 on OpenAlexafffund
Chen-Hsiang Ma, Kimberly B. Tworek, Janice Y. Kung, Sebastian Kilcommons, Kathleen E. Wheeler, Arabesque Parker, Janek Senaratne, Erika MacIntyre, Wendy Sligl, Constantine Karvellas, Fernando G. Zampieri, Demetrios J. Kutsogiannis, John Basmaji, Kimberley Lewis, Dipayan Chaudhuri, Sameer Sharif, Oleksa Rewa, Bram Rochwerg, Sean M. Bagshaw, Vincent Lau

Bibliographic record

VenueCritical Care Explorations · 2023
Typereview
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsImpactWestern UniversityAlberta Health ServicesMcMaster UniversityUniversity of AlbertaAlberta Health
FundersLondon Health Sciences Centre
KeywordsNonsteroidalMedicineMeta-analysisRandomized controlled trialIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

While opioids are part of usual care for analgesia in the ICU, there are concerns regarding excess use. This is a systematic review of nonsteroidal anti-inflammatory drugs (NSAIDs) use in postoperative critical care adult patients. DATA SOURCES: We searched Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, trial registries, Google Scholar, and relevant systematic reviews through March 2023. STUDY SELECTION: Titles, abstracts, and full texts were reviewed independently and induplicate by two investigators to identify eligible studies. We included randomized control trials (RCTs) that compared NSAIDs alone or as an adjunct to opioids for systemic analgesia. The primary outcome was opioid utilization. DATA EXTRACTION: In duplicate, investigators independently extracted study characteristics, patient demographics, intervention details, and outcomes of interest using predefined abstraction forms. Statistical analyses were conducted using Review Manager software Version 5.4. (The Cochrane Collaboration, Copenhagen, Denmark). DATA SYNTHESIS: = 1,621 patients) for admission to the ICU for postoperative management after elective procedures. Adjunctive NSAID therapy to opioids reduced 24-hour oral morphine equivalent consumption by 21.4 mg (95% CI, 11.8-31.0 mg reduction; high certainty) and probably reduced pain scores (measured by Visual Analog Scale) by 6.1 mm (95% CI, 12.2 decrease to 0.1 increase; moderate certainty). Adjunctive NSAID therapy probably had no impact on the duration of mechanical ventilation (1.6 hr reduction; 95% CI, 0.4 hr to 2.7 reduction; moderate certainty) and may have no impact on ICU length of stay (2.1 hr reduction; 95% CI, 6.1 hr reduction to 2.0 hr increase; low certainty). Variability in reporting adverse outcomes (e.g., gastrointestinal bleeding, acute kidney injury) precluded their meta-analysis. CONCLUSIONS: In postoperative critical care adult patients, systemic NSAIDs reduced opioid use and probably reduced pain scores. However, the evidence is uncertain for the duration of mechanical ventilation or ICU length of stay. Further research is required to characterize the prevalence of NSAID-related adverse outcomes.

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)0.023
metaresearch head score (Gemma)0.057
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 score0.028
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.057
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0280.036
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
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 head0.137
GPT teacher head0.434
Teacher spread0.296 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

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