USE OF AN ELECTRONIC DATA WAREHOUSE TO ENHANCE CARDIAC SURGICAL SITE SURVEILLANCE AT A LARGE CANADIAN CENTRE
Bibliographic record
Abstract
Background/Purpose: Surgical site infection surveillance to determineincidence is a key infection control activity. Case detection is labour-intensive, therefore most infection control programs use manual or simple electronic mechanisms to “trigger” chart review. However, such “trigger” mechanisms are also labour-intensive, and often of poor specificity. Our objective is to develop a complex trigger mechanism using data from an electronic data warehouse, to improve specificity of surveillance of surgical site infection compared to current trigger mechanisms. Methods: We will derive an electronic trigger tool for cardiac surgical site infection surveillance using a nested case-control design, among a cohort of all patients undergoing coronary artery bypass grafting, cardiac valve repairor replacement, or heart transplant at the University of Ottawa Heart Institute, from July 1 2004 to June 30 2007. We will perform a systematic literature review to identify potential trigger factors to include in the model, then construct the trigger tool by backwards stepwise logistic regression. The best-fit model will be used to calculate the probability of surgical site infection. We will select the threshold probability to use in surveillance by visual inspection of receiver-operator-characteristic curves. The accuracy of this electronic trigger mechanism will be compared to pre-existing manual and simple electronic mechanisms using relative true positive ratios and relative false positive ratios. Results/Conclusions: We have selected 200 cases of surgical site infection and 541 controls from among 3744 procedures performed during the study period. As of the date ofthis abstract we are still undertaking the systematic review.
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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 | 0.042 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".