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Record W2144025541 · doi:10.1002/jmri.21870

Impact of audio/visual systems on pediatric sedation in magnetic resonance imaging

2009· article· en· W2144025541 on OpenAlexaff
Colette Lemaire, Gerald Moran, Hans Swan

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2009
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsHamilton Health SciencesMcMaster Children's Hospital
Fundersnot available
KeywordsSedationMedicineMagnetic resonance imagingRadiologyImage qualityAnesthesiaImage (mathematics)Computer science

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the use of an audio/visual (A/V) system in pediatric patients as an alternative to sedation in magnetic resonance imaging (MRI) in terms of wait times, image quality, and patient experience. MATERIALS AND METHODS: Pediatric MRI examinations from April 8 to August 11, 2008 were compared to those 1 year prior to the installation of the A/V system. Data collected included age, requisition receive date, scan date, and whether sedation was used. A posttest questionnaire was used to evaluate patient experience. Image quality was assessed by two radiologists. RESULTS: Over the 4 months in 2008 there was an increase of 7.2% (115; P < 0.05) of pediatric patients scanned and a decrease of 15.4%, (67; P = 0.32) requiring sedation. The average sedation wait time decreased by 33% (5.8 months) (P < 0.05). Overall, the most positively affected group was the 4-10 years. The questionnaire resulted in 84% of participants expressing a positive reaction to the A/V system. Radiological evaluation revealed no changes in image quality between A/V users and sedates. CONCLUSION: The A/V system was a successful method to reduce patient motion and obtain a quality diagnostic MRI without the use of sedation in pediatric patients. It provided a safer option, a positive experience, and decreased wait times.

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)0.001
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 score0.215
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.013
GPT teacher head0.304
Teacher spread0.291 · 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

Citations78
Published2009
Admission routes1
Has abstractyes

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