A Comparison of Administrative Data Versus Surveillance Data for Hospital-Associated Methicillin-Resistant <i>Staphylococcus aureus</i> Infections in Canadian Hospitals
Bibliographic record
Abstract
BACKGROUND In Canadian hospitals, clinical information is coded according to national coding standards and is routinely collected as administrative data. Administrative data may complement active surveillance programs by providing in-hospital MRSA infection data in a standardized and efficient manner, but only if infections are accurately captured. OBJECTIVE To assess the accuracy of administrative data regarding in-hospital bloodstream infections (BSIs) and all-body-site infections due to MRSA. METHODS A retrospective study of all (adult and pediatric) in-hospital MRSA infections was conducted by comparing administrative data against surveillance data from 217 acute Canadian hospitals (124 in Ontario, 93 in Alberta) over a 12-month period. Hospital-associated MRSA BSI cases in Ontario, and for all-body-site MRSA infections in Alberta were identified. Pearson correlation coefficients were used to compare the number of hospital-level MRSA cases within administrative versus surveillance datasets. The correlation of all-body-site MRSA infections versus MRSA BSIs was also assessed using the Ontario administrative data. RESULTS Strong correlations between hospital-level MRSA cases in administrative and surveillance datasets were identified for Ontario (r=0.79; 95% CI, 0.72-0.85) and Alberta (r=0.92; 95% CI, 0.88-0.94). A strong correlation between all-body-site and bloodstream-only MRSA infection rates was identified across Ontario hospitals (r=0.95; P<.0001; 95% CI, 0.93-0.96). CONCLUSIONS This study provides good evidence of the comparability of administrative and surveillance datasets in identifying in-hospital MRSA infections. With standard definitions, administrative data can provide estimates of in-hospital infections for monitoring and/or comparisons across hospitals. Infect Control Hosp Epidemiol 2017;38:436-443.
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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 | 0.016 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 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".