Risk factors for respiratory failure in Guillain‐Barré syndrome in Bangladesh: a prospective study
Bibliographic record
Abstract
Abstract Objective We investigated clinical, biological, and electrophysiological risk factors for mechanical ventilation (MV) and patient outcomes in Bangladesh using one of the largest, prospective Guillain‐Barré syndrome (GBS) cohorts in developing world. Methods A total of 693 GBS patients were included in two GBS studies conducted between 2006 and 2016 in Dhaka, Bangladesh. Associations between baseline characteristics and MV were tested using Fisher's exact test, χ 2 test, or Mann–Whitney U ‐test, as appropriate. Risk factors for MV were assessed using multivariate logistic regression. Survival analysis was performed using Kaplan–Meier method; comparisons between groups performed using log‐rank test. Results Of 693 patients, 155 (23%) required MV (median age, 26 years; interquartile range [IQR] 17–40). Among the ventilated patients, males were predominant (68%) than females. The most significant risk factor for MV was bulbar involvement (adjusted odds ratio [AOR]:19.07; 95% CI = 89.00–192.57, P = 0.012). Other independently associated factors included dysautonomia (AOR:4.88; 95% CI = 1.49–15.98, P = 0.009) and severe muscle weakness at study entry (AOR:6.12; 95% CI = 0.64–58.57, P = 0.048). At 6 months after disease onset, 20% of ventilated and 52% of non‐ventilated patients ( P < 0.001) had recovered completely or with minor symptoms. Mortality rate was significantly higher among ventilated patients than non‐ventilated patients (41% vs. 7%, P < 0.001). Interpretation Bulbar involvement, dysautonomia and severe muscle weakness were identified as the most important risk factors for MV among GBS patients from Bangladesh. The findings may help to develop predictive models for MV in GBS in developing countries to identify impending respiratory failure and proper clinical management of GBS patients.
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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 | 0.001 |
| 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 | 0.000 | 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".