No evidence of a link between influenza vaccines and Guillain–Barre syndrome–associated antiganglioside antibodies
Bibliographic record
Abstract
BACKGROUND: Guillain-Barre syndrome (GBS) is a rare autoimmune disease characterized by acute, progressive peripheral neuropathy and is commonly associated with the presence of antiganglioside antibodies. Previously, influenza vaccination was linked with the increased incidence of GBS; however, whether antiganglioside antibodies are subsequently induced remains unresolved. METHODS: Sera from human subjects vaccinated with seasonal influenza vaccines from the 2007-2008, 2008-2009, or 1976-1977 influenza seasons were screened for the induction of immunity to influenza and the presence of antiganglioside antibodies pre- and post-vaccination. Likewise, sera from mice vaccinated with seasonal influenza vaccines (1988-1989, 2007-2008) or "swine flu" pandemic vaccines (1976, 2009) were assessed in the same manner. Viruses were also screened for cross-reacting ganglioside epitopes. RESULTS: Antiganglioside antibodies were found to recognize influenza viruses; this reactivity correlated with virus glycosylation. Antibodies to influenza viruses were detected in human and mouse sera, but the prevalence of antiganglioside antibodies was extremely low. CONCLUSIONS: Although the correlation between antiganglioside antibody cross-reactivity and glycosylation of viruses suggests the role of shared carbohydrate epitopes, no correlation was observed between hemagglutinin-inhibition titers and the induction of antiganglioside antibodies after influenza vaccination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".