D.4 International consensus recommendations for the management of glucocorticoid complications in neuromuscular disease
Bibliographic record
Abstract
Background: Adverse effects and risks associated with glucocorticoid (GC) treatment are frequently encountered in immune-mediated neuromuscular disorders. However, significant variability exists in the management of these complications. Our aim was to establish international consensus guidance on the management of GC-related complications in neuromuscular disorders. Methods: An international task force of 15 experts was assembled to develop clinical recommendations for managing GC-related complications in neuromuscular patients. The RAND/UCLA Appropriateness Method (RAM) was employed to formulate consensus guidance statements. Initial statements were drafted following a comprehensive literature review and were refined based on anonymous expert feedback, with up to three rounds of email voting to achieve consensus. Results: Consensus was reached on statements addressing general patient care, monitoring during GC therapy, osteoporosis prevention, vaccinations, infection screening, and prophylaxis for Pneumocystis jiroveci pneumonia. A multidisciplinary approach to managing GC-related complications was highlighted as a key recommendation. Conclusions: This represents the first consensus guidance in the neurological literature on GC complications, and offer clinicians structured guidance on mitigating and managing common adverse effects associated with both short- and long-term GC use. They also provide a foundation for future debate, quality improvement, research work in this area.
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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 | 0.078 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".