Peripheral nervous system and neuromuscular disorders in the emergency department: A review
Bibliographic record
Abstract
INTRODUCTION: Acute presentations and emergencies in neuromuscular disorders (NMDs) often challenge clinical acumen. The objective of this review is to refine the reader's approach to history taking, clinical localization and early diagnosis, as well as emergency management of neuromuscular emergencies. METHODS: An extensive literature search was performed to identify relevant studies. We prioritized meta-analysis, systematic reviews, and position statements where possible to inform any recommendations. SUMMARY: The spectrum of clinical presentations and etiologies ranges from neurotoxic envenomation or infection to autoimmune disease such as Guillain-Barré Syndrome (GBS) and myasthenia gravis (MG). Delayed diagnosis is not uncommon when presentations occur "de novo," respiratory failure is dominant or isolated, or in the case of atypical scenarios such as GBS variants, severe autonomic dysfunction, or rhabdomyolysis. Diseases of the central nervous system, systemic and musculoskeletal disorders can mimic presentations in neuromuscular disorders. CONCLUSIONS: Fortunately, early diagnosis and management can improve prognosis. This article provides a comprehensive review of acute presentations in neuromuscular disorders relevant for the emergency physician.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".