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Peri‐operative intravenous administration of magnesium sulphate and postoperative pain: a meta‐analysis

2012· review· en· W2134589566 on OpenAlexaff
Éric Albrecht, Kyle R. Kirkham, S LIU, Richard Brull

Bibliographic record

VenueAnaesthesia · 2012
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsToronto Western HospitalWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineAnesthesiaMagnesiumPlaceboPerioperativeAdverse effectBolus (digestion)MorphinePostoperative painSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Intravenous magnesium has been reported to improve postoperative pain; however, the evidence is inconsistent. The objective of this quantitative systematic review is to evaluate whether or not the peri-operative administration of intravenous magnesium can reduce postoperative pain. Twenty-five trials comparing magnesium with placebo were identified. Independent of the mode of administration (bolus or continuous infusion), peri-operative magnesium reduced cumulative intravenous morphine consumption by 24.4% (mean difference: 7.6 mg, 95% CI -9.5 to -5.8 mg; p < 0.00001) at 24 h postoperatively. Numeric pain scores at rest and on movement at 24 h postoperatively were reduced by 4.2 (95% CI -6.3 to -2.1; p < 0.0001) and 9.2 (95% CI -16.1 to -2.3; p = 0.009) out of 100, respectively. We conclude that peri-operative intravenous magnesium reduces opioid consumption, and to a lesser extent, pain scores, in the first 24 h postoperatively, without any reported serious adverse effects.

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)0.004
metaresearch head score (Gemma)0.012
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: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.017
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.340
Teacher spread0.265 · 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

Citations399
Published2012
Admission routes1
Has abstractyes

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