Predictors of Mechanical Ventilation in Guillain–Barré Syndrome with Axonal Subtypes
Bibliographic record
Abstract
BACKGROUND: The early clinical predictors of respiratory failure in Latin Americans with Guillain-Barré syndrome (GBS) have scarcely been studied. This is of particular importance since Latin America has a high frequency of axonal GBS variants that may imply a worse prognosis. METHODS: , a referral center of Mexico City, to describe predictors of invasive mechanical ventilation (IMV). RESULTS: The median age was 40 years (interquartile range: 26-53.5), with 60.5% men (male-to-female ratio: 1.53). Most patients (65%) had an infectious antecedent (40.6% gastrointestinal). At admission, 38% of patients had a Medical Research Council (MRC) sum score <30. Axonal subtypes predominated (60.5%), with acute motor axonal neuropathy being the most prevalent (34.9%), followed by acute inflammatory demyelinating polyneuropathy (32.6%), acute motor sensory axonal neuropathy (AMSAN) (25.6%), and Fisher syndrome (7%). Notably, 15.1% had onset in upper limbs, 75.6% dysautonomia, and 73.3% pain. In all, 86% received either IVIg (9.3%) or plasma exchange (74.4%). IMV was required in 39.5% patients (72.7% in AMSAN). A multivariate model without including published prognostic scores yielded the time since onset to admission <15 days, axonal variants, MRC sum score <30, and bulbar weakness as independent predictors of IMV. The model including grading scales yielded lower limbs onset, Erasmus GBS respiratory insufficiency score (EGRIS) >4, and dysautonomia as predictors. CONCLUSION: These results suggest that EGRIS is a good prognosticator of IMV in GBS patients with a predominance of axonal electrophysiological subtypes, but other early clinical data should also be considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".