A protocol for a scoping review on the role of whole-body and dedicated body-part magnetic resonance imaging for assessment of adult and juvenile idiopathic inflammatory myopathies
Bibliographic record
Abstract
ABSTRACT Background Currently, there is lack of standardization of magnetic resonance imaging (MRI) scoring systems and protocols for assessment of idiopathic inflammatory myopathies (IIMs) in children and adults among treatment centres across the globe. Therefore, we will perform scoping reviews of the literature to inform available semi-quantitative and quantitative MRI scoring systems and protocols for the assessment and monitoring of skeletal muscle involvement in patients with IIMs with the final goal of providing evidence-based information for the future development of a universal standardized MRI scoring system in specific research and clinical settings in this population. Methods Electronic databases (PubMed, EMBASE, and Cochrane) will be searched to select relevant articles published from January 2000 to October 2023. Data will be synthesized narratively. Discussion This scoping review will extensively map evidence on the indications, utility for diagnosis and assessment of disease activity and damage using skeletal muscle MRI in IIMs. The results will allow the development of consensus recommendations for clinical practice and enable the standardization of research methods for MRI assessment of skeletal muscle changes in patients with IIMs.
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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 | 0.101 | 0.157 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.086 | 0.013 |
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".