International Consensus Guidance for the Management of Glucocorticoid Related Complications in Neuromuscular Disease
Bibliographic record
Abstract
INTRODUCTION/AIMS: Glucocorticoid (GC)-related adverse reactions and risks are commonly seen during the treatment of immune-mediated and inflammatory neuromuscular disorders. There is wide variation in the management of associated complications. The aim of this study is to develop international consensus guidance on the management of GC-related complications in neuromuscular disorders. METHODS: Through the American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM), an international task force of 15 experts was convened to develop clinical guidance for the management of GC-related complications in neuromuscular patients. The RAND/UCLA appropriateness method (RAM) was used to develop consensus guidance statements. Initial guidance statements were crafted after a thorough literature review and were modified after anonymous panel input, with up to three rounds of voting via email to achieve consensus. RESULTS: Statements were developed and achieved consensus for general care, monitoring of patients while on GC, osteoporosis prevention, vaccinations, infection screening, and Pneumocystis jiroveci pneumonia prophylaxis. A multidisciplinary approach to the management of GC-related complications was emphasized. DISCUSSION: These formal consensus statements provide guidance to clinicians who use GC in the treatment of neuromuscular diseases regarding prevention and management of the more common associated adverse events and risks that arise with long and short-term GC use and serve as a springboard for investigation and updates.
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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 | 0.070 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".