Hepatitis B vaccine and risk of relapse after a first childhood episode of CNS inflammatory demyelination
Bibliographic record
Abstract
Public concern about possible increases in the risk of multiple sclerosis associated with hepatitis B vaccination has led to low vaccination coverage. We investigated whether this vaccination after a first episode of acute CNS inflammatory demyelination in childhood increased the risk of conversion to multiple sclerosis. We studied the French Kid Sclérose en Plaques (KIDSEP) neuropaediatric cohort of patients enrolled between 1994 and 2003 from their first episode of acute CNS inflammatory demyelination (inclusion in the cohort) until the occurrence of a second episode, up to 2005. A Cox proportional hazards model of time-dependent vaccine exposure was used to evaluate the effect of vaccination (hepatitis B, tetanus) during follow-up on the risk of second episode occurrence (conversion to multiple sclerosis). The cohort included 356 subjects with a mean follow-up of 5.8 years (SD 2.7). Relapse occurred in 146 (41%) subjects during follow-up; 33 subjects were exposed to hepatitis B vaccine and 28 to tetanus vaccine at some time during follow-up. The adjusted hazard ratio (HR) for relapse occurring within 3 years of hepatitis B vaccination was 0.78 (0.32-1.89) and during any time period was 1.09 (0.53-2.24). The adjusted HR for relapse occurring within 3 years of tetanus vaccination was 0.99 (0.58-1.67) and during any time period was 1.08 (0.63-1.83). We conclude that vaccination against hepatitis B or tetanus after a first episode of CNS inflammatory demyelination in childhood does not appear to increase the risk of conversion to multiple sclerosis, although the possibility of a small increase in risk cannot be excluded.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".