The feasibility and acceptability of research magnetic resonance imaging in adolescents with moderate–severe neuropathic pain
Bibliographic record
Abstract
INTRODUCTION: Multimodal characterisation with questionnaires, Quantitative Sensory Testing (QST), and neuroimaging will improve understanding of neuropathic pain (NeuP) in adolescents. Magnetic resonance imaging (MRI) data in adolescents with NeuP are limited, and the perceived practical or ethical burden of scanning may represent a barrier to research. OBJECTIVE: To determine the feasibility of MRI scanning in adolescents with moderate-severe NeuP, with respect to consent rate, postscan acceptability, and data quality. METHODS: This prospective cohort study evaluating questionnaires and QST recruited adolescents aged 10 to 18 years with clinically diagnosed NeuP from a tertiary clinic. Eligible adolescents aged 11 years and older could additionally agree/decline an MRI scan. After the scan, families rated discomfort, perceived risk, and acceptability of current and future MRI scans (0-10 numerical rating scales). Head motion during scanning was compared with healthy controls to assess data quality. RESULTS: Thirty-four families agreed to MRI (72% recruitment), and 21 adolescents with moderate-severe pain (average last week 6.7 ± 1.7; mean ± SD) and with neuropathic QST profiles were scanned. Three adolescents reported positional or noise-related discomfort during scanning. Perceived risk was low, and acceptability of the current scan was high for parents (range [median]: 7 to 10/10 [10]) and adolescents (8-10/10 [10]). Willingness to undergo a future research scan was high for parents (7-10/10 [10]) and adolescents (5-10/10 [10]) and did not differ from future scans for clinical purposes. Mean head motion during resting state functional MRI did not differ from control adolescents. CONCLUSION: Research MRI is feasible and acceptable for many adolescents with moderate-severe NeuP.
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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 | 0.007 | 0.021 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".