The utility of measles and rubella IgM serology in an elimination setting, Ontario, Canada, 2009–2014
Bibliographic record
Abstract
In Canada, measles was eliminated in 1998 and rubella in 2000. Effective measles and rubella surveillance is vital in elimination settings, hinging on reliable laboratory methods. However, low-prevalence settings affect the predictive value of laboratory tests. We conducted an analysis to determine the performance of measles and rubella IgM testing in a jurisdiction where both infections are eliminated. 21,299 test results were extracted from the Public Health Ontario Laboratories database and 1,239 reports were extracted from the Ontario Integrated Public Health Information System (iPHIS) from 2008 and 2010 for measles and rubella, respectively, to 2014. Deterministic linkage resulted in 658 linked measles records (2009-2014) and 189 linked rubella records (2010-2014). Sixty-six iPHIS measles entries were classified as confirmed cases, of which 53 linked to laboratory data. Five iPHIS rubella entries were classified as confirmed, all linked to IgM results. The positive predictive value was 17.4% for measles and 3.6% for rubella. Sensitivity was 79.2% for measles and 100.0% for rubella. Specificity was 65.7% for measles and 25.8% for rubella. Our study confirms that a positive IgM alone does not confirm a measles case in elimination settings. This has important implications for countries that are working towards measles and rubella elimination.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".