An assessment of the validity and reliability of SARS-CoV-2 infection surveillance data from the Canadian Nosocomial Infection Surveillance Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it