Bibliographic record
Abstract
Measles, mumps, and rubella are infections caused by RNA viruses of the same name and are vaccine preventable. The vaccines are frequently administered in a trivalent form. Laboratory diagnostic methods can include indirect detection via antibody (IgM and IgG) detection methods and direct detection by viral culture or viral genome detection. There are challenges for the laboratory in areas with low prevalence due to high vaccine uptake. In those areas, routine serological methods such as IgM detection may have a reduced positive predictive value and thus require confirmation by other methods. Direct detection of viral genomic material using reverse transcription polymerase chain reaction (RT-PCR) methodologies can play an important role for laboratory confirmation of acute infections. Furthermore, genotyping of these three viruses provides useful molecular epidemiological data for differentiating vaccine from wild-type strains, linking cases and outbreaks, and tracking geographic spread and elimination. The purpose of this chapter is to provide guidance for the laboratory diagnosis of measles, mumps, and rubella virus infections. Where assays are commercially available or previously published, the appropriate references are provided as well as brief comments on the interpretation of results. Detailed protocols are provided for the molecular assays which have been developed and more commonly applied in recent years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".