Evaluation of the Rubella Surveillance System in Quebec
Bibliographic record
Abstract
OBJECTIVE: To evaluate the validity of information in the rubella surveillance system in Quebec. DATA AND METHODS: Cases of rubella in the provincial registry of notifiable diseases, "Maladies à declaration obligatoire" (MADO), from 1994 to 1996 were matched with laboratory-identified cases and with cases in a reference file created from all case investigation records of regional departments of public health for the same period. Sensitivity and the proportion of cases in agreement were calculated. RESULTS: Compared with laboratories, the sensitivity of the provincial registry was 56%. Compared with the reference file, global sensitivity (confirmed cases plus clinical cases) was 58% and the positive predictive value was 50%. Of the 356 cases reported to regional public health departments, 65% were classified in the same diagnostic category (confirmed case, clinical case, excluded case) by public health professionals and a group of experts (weighted kappa=0.32). Information on rubella vaccination status was missing in 25% of cases in the MADO file for rubella. CONCLUSIONS: Notification of positive results for immunoglobulin M antibodies and viral cultures should be required of all laboratories. Uniform procedures should be adopted and applied for the validation of cases that are reported to regional departments of public health. In the context of the rarefaction of rubella, any immunoglobulin M-positive result should be interpreted using all available epidemiological information.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".