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Propofol total intravenous anesthesia for MRI in children

2004· article· en· W1972354717 on OpenAlexafffund
Andrew G. Usher, Ramona A. Kearney, Ban C. H. Tsui

Bibliographic record

VenuePediatric Anesthesia · 2004
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
FundersUniversity of Alberta
KeywordsMedicinePropofolAnesthesia

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to assess clinical signs of airway patency, airway intervention requirements and adverse events in 100 children receiving propofol total intravenous anesthesia for magnetic resonance imaging, with spontaneous ventilation and oxygenation via nasal prongs. METHODS: Airway patency was clinically assessed and stepwise interventions were performed until a satisfactory airway was achieved. Propofol requirements, vital signs, procedure times and adverse events were also recorded. RESULTS: Ninety-three per cent of children had no signs of airway obstruction when positioned with a shoulder roll only, two required a chin lift, four required an oral airway and one required lateral positioning. The mean propofol induction dose was 3.9 mg.kg(-1) (range 1.8-6.4 mg.kg(-1)). The mean propofol infusion rate was 193 microg.kg(-1).min(-1) (range 150-250 microg.kg(-1).min(-1)). The initial and final mean respiratory rates were 26 and 23 b.min(-1) (P < 0.05). Movement was more likely at lower infusion rates (mean 175 microg.kg(-1).min(-1)). There were no respiratory or cardiovascular complications (calculated risk: 95% CI = 0-3%). The mean time from end of scan to discharge home was 44 min. CONCLUSIONS: This study demonstrates good preservation of upper airway patency and rapid recovery using general anesthetic doses of propofol in children.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0010.000

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 head0.244
Teacher spread0.235 · 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 designNon-randomized trial
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

Citations77
Published2004
Admission routes2
Has abstractyes

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