Heterogeneity of Rotavirus Testing and Admitting Practices for Gastroenteritis among 12 Tertiary Care Pediatric Hospitals: Implications for Surveillance
Bibliographic record
Abstract
BACKGROUND: The Canadian Immunization Monitoring Program, ACTive (IMPACT) surveillance for rotavirus relies on monitoring hospital admissions. Because a diagnosis of rotavirus is not necessary for treatment purposes, and rotavirus is not a reportable disease, wide variation may exist in the admitting and testing practices for this disease. From 2005 to 2007, the number of rotavirus admissions differed significantly among IMPACT centres, and this variation could not be explained by population differences alone. Understanding this variation is important when interpreting surveillance data and estimating the cost-effectiveness of rotavirus vaccination programs. METHODS: Key informant interviews were conducted with pediatric infectious disease physicians and IMPACT nurse monitors involved with rotavirus surveillance to obtain in-depth information about rotavirus testing and admitting practices at each of the 12 IMPACT centres. RESULTS: A total of 18 of 24 interviews were completed, with at least one interview conducted with physicians and/or nurses at each centre. Four major differences were identified among the centres: case-identification methods, admission definitions, admission criteria and testing criteria. The criteria for admitting and testing patients as well as which patients were defined as admissions had the greatest influence on case totals. DISCUSSION: The present study found that differences in admitting and testing practices may contribute to significant differences in rotavirus admission totals. Given these differences, caution should be used when using local case estimates for cost-effectiveness analyses and immunization program decisions. The present study illustrates that understanding the factors that influence the identification of a disease is important when interpreting and applying surveillance data.
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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 | 0.020 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".