Mumps Outbreaks in Canada and the United States: Time for New Thinking on Mumps Vaccines
Bibliographic record
Abstract
Mumps epidemics in Canada and the United States prompted us to review evidence for the effectiveness of 5 different vaccine strains. Early trials with the Jeryl Lynn vaccine strain demonstrated an efficacy of approximately 95%, but in epidemic conditions, the effectiveness has been as low as 62%; this is still considerably better than the effectiveness of another safe strain, Rubini (which has an effectiveness of close to 0% in epidemic conditions). The Urabe vaccine strain has an effectiveness of 54%-87% but is prone to cause aseptic meningitis. Little epidemiological information is available for other vaccines. The Leningrad-Zagreb vaccine strain, which is widely used in developing countries and costs a fraction of what vaccines cost in the developed world, seems to have encouraging results; in 1 study, the effectiveness of this vaccine exceeded 95%. Aseptic meningitis has also been reported in association with this vaccine, but the benign nature of the associated meningitis was shown recently in Croatia. Also, the Leningrad-3 strain seems to be effective but causes less-benign meningitis. No mumps vaccine equals the best vaccines in quality, but the virtually complete safety of some strains may not offset their low effectiveness. Epidemiological data are pivotal in mumps, because serological testing is subject to many interpretation problems.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".