Practice MRI: Reducing the need for sedation and general anaesthesia in children undergoing MRI
Bibliographic record
Abstract
The aim of this study was to evaluate the effectiveness of a practice magnetic resonance unit, in preparing children to undergo magnetic resonance procedures without general anaesthesia (GA) or sedation. The records of children who attended the practice MRI between February 2002 and April 2004 were retrospectively reviewed. Each record was assessed as to whether the child had passed or failed the practice MRI intervention. Those children who were considered to have passed and were proceeded to a clinical non-GA MRI had the report of the clinical scan reviewed. If the scan had been reported as non-diagnostic because of movement artefact it was classified as a failed scan, otherwise it was considered a pass. One hundred and thirty-four children undertook a practice MRI (age range 4.1-16.1 years, median age 7.7 years, 47% boys) and 120/134 (90%) passed the practice session. In all, 117/120 (98%) subsequently had a clinical non-GA MRI and 110/117 (94%) passed (median age 7.8 years, 47% boys). Preparation is a safe and effective method to reduce the need for sedation and GA in children undergoing a clinical MRI scan. It provides a positive medical experience for children, parents and staff, and results in cost savings for the hospital.
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 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 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".