Impact of audio/visual systems on pediatric sedation in magnetic resonance imaging
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".