An assessment of the validity and reliability of SARS-CoV-2 infection surveillance data from the Canadian Nosocomial Infection Surveillance Program
Bibliographic record
Abstract
Background: Tracking healthcare-associated infections (HAIs) is crucial for reducing and preventing transmission. This study aimed to evaluate the validity and reliability of the Canadian Nosocomial Infection Surveillance Program (CNISP) COVID-19 surveillance data by assessing key metrics, including case definition, case classification, and outcomes. Methods: In December 2022, a survey containing 12 COVID-19 case study questions was administered to staff from 81 eligible hospitals across 32 hospital networks. These staff members were responsible for submitting data using a standardized protocol and case definitions. Results: Fifty-four (67%) of the 81 CNISP hospital sites completed the survey. The mean survey score was 79% with a median of 83%, and a range of 58-91%. Scores varied by question theme, from 70% for reasons for admission, to 93% for multiple positives. Conclusion: The study findings indicate that CNISP case definitions and classifications were consistently and accurately applied across most case study questions. These results underscore the robust quality of COVID-19 data gathered through the national surveillance platform.
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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 | 0.046 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".