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Determination of the median effective concentration (EC<sub>50</sub>) of propofol during oesophagogastroduodenoscopy in children

2001· article· en· W1980226334 on OpenAlexaff
Gregory B. Hammer, Catherine Litalien, Vinit Wellis, David R. Drover

Bibliographic record

VenuePediatric Anesthesia · 2001
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsPropofolMedicineAnesthesiaPlasma concentrationPharmacokineticsPremedicationBlood pressureHeart rateInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Propofol is commonly used to provide anaesthesia for children undergoing oesophagogastroduodenoscopy (OGD). Despite this, the plasma concentration-response relationships for propofol used in this setting have not been established. METHODS: In order to determine the EC50 of propofol during OGD, we studied 12 children aged 3-10 years. No premedication was given. Propofol was administered via a target-controlled infusion system using the STANPUMP software based on a paediatric pharmacokinetic model. The 'up-and-down' method described by Dixon was used to determine the EC50. Accordingly, the target plasma propofol concentration for each patient, beginning with the second subject, was determined by the response of the previous patient. A patient was considered a 'responder' if there was minimal movement and the heart rate (HR) and blood pressure (BP) remained < or = 120% of baseline during the procedure. Patients who moved excessively, i.e. requiring more than gentle restraint, or who manifest HR and BP >120% of baseline, were considered 'nonresponders'. RESULTS: The EC50 of propofol during OGD was 3.55 microg.ml(-1) in this study. CONCLUSIONS: The plasma propofol concentration associated with adequate anaesthesia for OGD in 50% of unpremedicated children is 3.55 microg.ml(-1). This concentration is higher than that required for OGD in adult 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 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 score0.065
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.228
Teacher spread0.223 · 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

Citations33
Published2001
Admission routes1
Has abstractyes

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