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The analgesic efficacy and safety of neuraxial magnesium sulphate: a quantitative review

2012· review· en· W1499296304 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
KeywordsMedicineAnalgesicPlaceboAnesthesiaMagnesiumClinical trialAdverse effectSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Eighteen published trials have examined the use of neuraxial magnesium as a peri-operative adjunctive analgesic since 2002, with encouraging results. However, concurrent animal studies have reported clinical and histological evidence of neurological complications with similar weight-adjusted doses. The objectives of this quantitative systematic review were to assess both the analgesic efficacy and the safety of neuraxial magnesium. Eighteen trials comparing magnesium with placebo were identified. The time to first analgesic request increased by 11.1% after intrathecal magnesium administration (mean difference: 39.6 min; 95% CI 16.3-63.0 min; p = 0.0009), and by 72.2% after epidural administration (mean difference: 109.5 min; 95% CI 19.6-199.3 min; p = 0.02) with doses of between 50 and 100 mg. Four trials monitored for neurological complications: of the 140 patients included, only a 4-day persistent headache was recorded. Despite promising peri-operative analgesic effect, the risk of neurological complications resulting from neuraxial magnesium has not yet been adequately defined.

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)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0070.008
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.346
Teacher spread0.287 · 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 designSystematic review
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

Citations64
Published2012
Admission routes1
Has abstractyes

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