Vaccine-associated inflammatory diseases of the central nervous system
Bibliographic record
Abstract
PURPOSE OF REVIEW: As the most cost-effective intervention in preventive medicine and as a crucial element of any public health program, vaccination is used extensively with over 90% coverage in many countries. As approximately 5-8% of the population in developed countries suffer from an autoimmune disorder, people with an autoimmune disease are most likely to be exposed to some vaccines before or after the disease onset. In fact, a number of inflammatory disorders of the central nervous system have been associated with the administration of various vaccines. These adverse events, be they spurious associations or genuine reactions to the vaccine, may lead to difficulties in obtaining public trust in mass vaccination programs. There is, thus, an urgent need to understand whether vaccination triggers or enhances autoimmune responses. RECENT FINDINGS: By reviewing vaccine-associated inflammatory diseases of the central nervous system, this study describes the current knowledge on whether the safety signal was coincidental, as in the case of multiple sclerosis with several vaccines, or truly reflected a causal link, as in narcolepsy with cataplexy following pandemic H1N1 influenza virus vaccination. SUMMARY: The lessons learnt emphasize a central role of thorough, ideally prospective, epidemiological studies followed, if the signal is deemed plausible or real, by immunological investigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 0.001 |
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".