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Combined propofol and remifentanil intravenous anesthesia for pediatric patients undergoing magnetic resonance imaging

2005· article· en· W1976264703 on OpenAlexafffund
Ban C. H. Tsui, Alese Wagner, Andrew G. Usher, Dominic Cave, Cathy Tang

Bibliographic record

VenuePediatric Anesthesia · 2005
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMedicineRemifentanilPropofolAnesthesiaVomitingMagnetic resonance imagingPostoperative nausea and vomitingRespiratory rateNauseaSurgeryBlood pressureHeart rateRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: A prospective observational case series of children receiving light general anesthesia for magnetic resonance imaging (MRI) was performed. Our purpose was to examine the merit of anesthesia and recovery/discharge times of combined remifentanil and propofol total intravenous anesthesia (TIVA) in spontaneously breathing children. METHODS: After IRB approval and informed consent, 56 patients receiving Remi/Propofol TIVA (Remifentanil 10 microg.ml(-1) Propofol 10 mg.ml(-1)) were observed. Blood pressure, respiratory rate, endtidal CO(2) (P(E)CO(2)), oxygen saturation and temperature were recorded at the start and finish of anesthesia. In addition, induction and recovery times were noted. Recovery time was from scan completion until discharge from the initial recovery area. Discharge time was from scan completion to discharge home. RESULTS: Fifty-six patients received Remi/Propofol TIVA. The mean Remi/Propofol recovery and discharge times were 8.9 and 28.2 min, respectively. There was a statistically significant decrease in respiratory rate and increase in CO(2) from the start to the end of the procedure. During the scan, seven patients moved. One patient experienced postprocedure nausea and or vomiting. CONCLUSIONS: The combination of remifentanil and propofol for TIVA may be an effective method of light general anesthesia in pediatric patients undergoing MRI.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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.

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

Citations48
Published2005
Admission routes2
Has abstractyes

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