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The Pharmacoeconomics of Neuromuscular Blocking Drugs: A Perioperative Cost-Minimization Strategy in Children

2001· article· en· W2016469077 on OpenAlexaff
William M. Splinter, Lisa Isaac

Bibliographic record

VenueAnesthesia & Analgesia · 2001
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineNeuromuscular Blocking AgentsNeuromuscular BlockadeAnesthesiaRocuroniumAnestheticPropofolAmbulatoryPerioperativeNeuromuscular monitoringDrugVecuronium bromideMuscle relaxantPharmacoeconomicsIntensive care medicineSurgeryPharmacology

Abstract

fetched live from OpenAlex

UNLABELLED: The purpose of this investigation was to compare the costs of intermediate-acting neuromuscular blocking drugs in children during routine ambulatory surgery. We studied 200 healthy, 2-10-yr-old children undergoing elective dental restorative surgery. During Part 1 of the study, children received an inhaled anesthetic with halothane and nitrous oxide, whereas in Part 2, the anesthetic was IV propofol with nitrous oxide. The study drugs were atracurium, cisatracurium, mivacurium, rocuronium, and vecuronium. Patients were initially administered 2x the effective dose for 95% of the study drug. After recovery to 10% of baseline neuromuscular function, the neuromuscular blockade was rigidly maintained with an infusion of the study drug at about 10% of baseline function. Neuromuscular drug costs were approximated as drug usage x cost/unit. The initial drug costs were not substantially different for both Parts 1 and 2, but over time, mivacurium became the most expensive drug and cisatracurium the least expensive. In conclusion, based on current costs, cisatracurium is the least expensive intermediate-acting neuromuscular drug. IMPLICATIONS: For children undergoing minor ambulatory procedures of 1-2 h, and continuous intraoperative neuromuscular blockade is indicated, cisatracurium currently is the least expensive drug.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2001
Admission routes1
Has abstractyes

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