Factors Predicting Prolonged Mechanical Ventilation in Guillain–Barré Syndrome
Bibliographic record
Abstract
ABSTRACT: Background: Up to 30% of patients with Guillain–Barré syndrome require mechanical ventilation and 5% die due to acute complications of mechanical ventilation. There is a considerable group of patients that will need prolonged mechanical ventilation (considered as >14 days) and should be considered for early tracheostomy. The objective of this study is to identify risk factors for prolonged mechanical ventilation. Methods: We prospectively analyzed patients with Guillain–Barré diagnosis with versus without prolonged mechanical ventilation. We considered clinical and electrophysiological characteristics and analyzed factors associated with prolonged mechanical ventilation. Results: Three hundred and three patients were included; 29% required mechanical ventilation. When comparing the groups, patients with prolonged invasive mechanical ventilation (IMV) have a lower score on the Medical Research Council score (19.5 ± 16.2 vs 27.4 ± 17.5, p = 0.03) and a higher frequency of dysautonomia (42.3% vs 19.4%, p = 0.037), as well as lower amplitudes of the distal compound muscle action potential (CMAP) of the median nerve [0.37 (RIQ 0.07–2.25) vs. 3.9 (RIQ1.2–6.4), p = <0.001] and ulnar nerve [0.37 (RIQ0.0–3.72) vs 1.5 (RIQ0.3–6.6), p = <0.001], and higher frequency of severe axonal damage in these nerves (distal CMAP ≤ 1.0 mV). Through binary logistic regression, severe axonal degeneration of the median nerve is an independent risk factor for prolonged IMV OR 4.9 (95%CI 1.1–21.5) p = 0.03, AUC of 0.774, (95%CI 0.66–0.88), p = < 0.001. Conclusions: Severe median nerve damage is an independent risk factor for prolonged mechanical ventilation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".