Evaluation of the accuracy in the application of the Canadian Nosocomial Infection Surveillance Program (CNISP) bloodstream infection surveillance definitions
Bibliographic record
Abstract
Background: There is an ongoing need to track and prevent infections acquired within healthcare facilities. The goal of this study was to evaluate the validity and reliability of Canadian Nosocomial Infection Surveillance Program (CNISP) healthcare-associated (HA) bloodstream infection (BSI) surveillance data by assessing the application of case definitions. Methods: In September 2019, a survey with nine BSI case studies and 19 associated questions was provided to staff from 78 eligible hospitals, representing 40 hospital networks. Results: A total of 723 responses were received from 58% of CNISP sites. Correct responses were reported as a proportion of all responses, with a mean survey score of 87.6% (Median, 89.5%, Range, 52.6%-100%). Scores were similar across all question types: case definition, case classification, and source of infection (88.5%, 84.5% and 88.2% respectively). Conclusion: CNISP case definitions, case classifications and criteria for source of infection were correctly and consistently applied in most case scenarios, highlighting the high quality of BSI surveillance data collected through CNISP. Ongoing data quality reviews to check inter-rater interpretation are important to ensure the validity and reliability of national surveillance of healthcare-acquired infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.106 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".