Pandemic H1N1 Vaccination and Incidence of Acute Disseminated Encephalomyelitis in Manitoba
Bibliographic record
Abstract
BACKGROUND: An increased incidence of hospital admissions coded as acute disseminated encephalomyelitis (ADEM) was noted in Winnipeg, Manitoba, Canada, during the second wave of the influenza pandemic from October 2009 to March 2010. However, it was not clear whether this was due to heightened awareness of potential neurological complications of influenza or influenza vaccination or an actual increase in the number of cases. METHODS: We extracted data from the charts of 139 patients hospitalized with an International Classification of Diseases-10 discharge code indicating ADEM (G04.0) or unspecified noninfectious encephalitis or myelitis (G04.8, G04.9) between January 2006 and December 2012. Clinical and laboratory data were reviewed by a neurologist, and diagnoses were determined using the Brighton criteria. RESULTS: Over the entire study period, there were 22 cases of ADEM. During the peak pandemic period (April-December 2009), seven patients were hospitalized with ADEM, corresponding to a rate of 7.8/million/year; 4.7 (95% confidence interval: 1.9-11.4) times higher than the rate before or after the pandemic period. Only one patient with ADEM had received the monovalent A(H1N1)pdm09 vaccine within 12 weeks of hospitalization. CONCLUSIONS: We have found an increased incidence of ADEM during the pandemic period that may be related, at least in part, to the increased incidence of influenza during that period. However, there was no temporal relationship with the administration of A(H1N1)pdm09 or seasonal influenza vaccines. Our study provides reassurance that use of these vaccines was not associated with increased risk of ADEM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".