Measles Outbreak in Unvaccinated and Partially Vaccinated Children and Adults in the United States and Canada (2018-2019): A Narrative Review of Cases
Bibliographic record
Abstract
Since 2018 and currently in 2019, the United States and Canada experienced a rapidly spreading measles virus outbreak. The developing outbreak may be due to a lack of vaccination, an inadequate dosage of measles (MMR) vaccine, clusters of intentionally under-vaccinated children, imported measles from global travel, and from those who are immunocompromised or have other life-threatening diseases. The infection originated mainly from travelers who acquired measles abroad and has thus led to a major outbreak and health concern not only in the United States and Canada but also in other parts of the world. According to World Health Organization, from January 2019 through September 2019, 1234 cases of measles have been reported in the United States and 91 reported cases in Canada, while in 2018, 372 and 28 cases were reported in the United States and Canada, respectively. A potential driving factor to the increased cases maybe because fewer children have been vaccinated over the last number of years in both countries. This article is a narrative review of cases discussing the measles outbreak among partially vaccinated and unvaccinated children and adults in the United States and Canada in 2018 and 2019.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".