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Record W4393234865 · doi:10.7759/cureus.57057

Comparison of Different Weight Scalars to Dose Sugammadex for the Reversal of Neuromuscular Blockade in Morbidly Obese Patients: A Systematic Review

2024· review· en· W4393234865 on OpenAlexaff
Yamini Subramani, Manikandan Rajarathinam, Anita Dabirzadeh, Qutaiba A. Tawfic, Sarah Krause, Yasin Avci, Mahesh Nagappa

Bibliographic record

VenueCureus · 2024
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsSugammadexMedicineRocuroniumNeuromuscular BlockadeMorbidly obeseAnesthesiaPopulationBody weightIncidence (geometry)Weight lossObesityInternal medicineIntubationMathematics

Abstract

fetched live from OpenAlex

This systematic review was conducted to evaluate the optimal weight scalar to dose sugammadex in a morbidly obese (MO) patient population (BMI≥40 kg/m2). The primary outcome was recovery time from moderate neuromuscular blockade (NMB) or deep NMB. Secondary outcomes included time to extubation and incidence of postoperative residual curarization (PORC). Eight randomized controlled trials (RCTs) involving 645 participants were included. The different dose scalars included were total body weight (TBW), ideal body weight (IBW), 20% corrected body weight (CBW) and 40% CBW). A dose of 2 mg/kg of sugammadex based on 40% CBW and a 4 mg/kg dose of sugammadex based on 40% CBW provide a reliable and timely reversal of moderate and deep NMB respectively in the MO patients.

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 score0.008
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
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 head0.073
GPT teacher head0.412
Teacher spread0.340 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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