Risk Stratification and Management of Acute Respiratory Failure in Patients With Neuromuscular Disease
Bibliographic record
Abstract
OBJECTIVES: Guillain-Barré syndrome (GBS) and myasthenia gravis (MG) are the most common causes of acute neuromuscular respiratory failure resulting in ICU admission. This synthetic narrative review summarizes the evidence for the prediction and management of acute neuromuscular respiratory failure due to GBS and MG. DATA SOURCES: We searched PubMed for relevant literature and reviewed bibliographies of included articles for additional relevant studies. STUDY SELECTION: English-language publications were reviewed. DATA EXTRACTION: Data regarding study methodology, patient population, evaluation metrics, respiratory interventions, and clinical outcomes were qualitatively assessed. DATA SYNTHESIS: No single tool has sufficient sensitivity and specificity for the prediction of acute neuromuscular respiratory failure requiring mechanical ventilation. Multimodal assessment, integrating history, examination maneuvers (single breath count, neck flexion strength, bulbar weakness, and paradoxical breathing) and pulmonary function testing are ideal for risk stratification. The Erasmus GBS Respiratory Insufficiency Score is a validated tool useful for GBS. Noninvasive ventilation can be effective in MG but may not be safe in early GBS. Airway management considerations are similar across both conditions, but dysautonomia in GBS requires specific attention. Extubation failure is common in MG, and early tracheostomy may be beneficial for MG. Prolonged ventilatory support is common, and good functional outcomes may occur even when prolonged ventilation is required. CONCLUSIONS: Multimodal assessments integrating several bedside indicators of bulbar and respiratory muscle function can aid in evidence-based risk stratification for respiratory failure among those with neuromuscular disease. Serial evaluations may help establish a patient's trajectory and to determine timing of respiratory intervention.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".