Awareness of bone strength in patients with neuromuscular disorders: ERN EURO-NMD clinician survey and European patient survey
Bibliographic record
Abstract
Bone strength is reduced In various neuromuscular disorders (NMDs). We aimed to assess the awareness and practice of bone strength management in NDMs among clinicians and patients. We performed two online surveys; among health care providers (HCPs) of the European Reference Network for Neuromuscular Disorders (ERN EURO-NMD) and among patients. The survey among 52 HCPs showed that awareness of potentially impaired bone strength in people with NMDs was reasonable to good: the vast majority of HCPs asked often or almost always about bone fractures during history-taking (81 %). Bone strength was less often assessed: often or almost always at diagnosis (50 %) and at follow-up (58 %). Medical training on this topic was considered poor to very poor in 50 % of HCPs. Prevention and treatment of reduced bone strength was variable and multidisciplinary care was sub-optimal. The survey among 581 patients provided important additional insights. Many patients were followed-up outside ERN EURO-NMD centers and treatment was variable. These parallel surveys provided a broad view on the awareness and management of bone strength in people with NMDs. The findings are expected to increase the appreciation of this important aspect of NMD care, and direct future research foci and care guidelines.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".