Chronic glucocorticoid management in neuromuscular disease: A survey of neuromuscular neurologists
Bibliographic record
Abstract
INTRODUCTION/AIMS: Glucocorticoids (GC) are first-line therapy for many neuromuscular diseases. There is a lack of guidelines regarding the prevention and management of GC complications in the context of neuromuscular disease, introducing the potential for practice variation, that may compromise quality of care. Our aim was to evaluate the practice patterns among Canadian adult neuromuscular neurologists on the screening, management, and treatment of GC-related complications and to identify variances in practice. METHODS: A web-based anonymous questionnaire was disseminated to 99 Canadian adult neuromuscular neurologists. Questions addressed patterns of screening, prevention, monitoring, and treatment of GC-induced adverse events, including infection prophylaxis, vaccination, bone health, hyperglycemia, and other complications. RESULTS: Seventy-one percent completed the survey. Of those, 52% perform screening blood work prior to initiating GC, 56% screen for infections, and 18% for osteoporosis. The majority monitor glycemic control and blood pressure (>85%). Thirty-two (46%) reported that they do not primarily monitor GC complications, but rather provide recommendations to the primary care physician. Pneumocystis jiroveci pneumonia prophylaxis was never used by 29%, and 29% recommend vaccinations prior to GC initiation. Calcium supplementation was recommended by 80% to prevent osteoporosis. Only 36% were aware of any existing guidelines for preventing GC complications, and 91% endorsed a need for neurology-specific guidelines. DISCUSSION: There is substantial variability in the management of GC adverse effects among neuromuscular neurologists, often not corresponding to limited published literature. Our results support the need for improved education and neurology-specific guidelines to help standardize practice and improve and prevent complications.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".