Reliability and feasibility of registered nurses conducting web‐based surgical site infection surveillance in the community: A prospective cohort study
Bibliographic record
Abstract
Surgical site infections increase health care costs, morbidity, and mortality in 2% to 5% of surgical patients. Standardised post-surgical surveillance is rare in community settings, causing under-reporting and under-serving of the documented 60% of surgical site infections occurring following hospital discharge. This study evaluated feasibility and concordance (inter-rater reliability) of paired registered nurses using a web-based surveillance tool (how2trakSSI, based on validated guidelines) to detect surgical site infections for up to 30 days after surgery in a cohort of 101 patients referred to Calea Home Care Clinics in Toronto, Canada, March 2015 to July 2016. After paired registered nurse assessors used the tool-less than 10 minutes apart to measure concordance 5 to 7 days postoperatively, they provided feedback on its usefulness at two teleconference discussion groups September 6 to 7, 2016. Overall concordance between assessors was 0.822, remaining consistently above 0.65 across assessor education level and experience, patient age and weight, and wound area. Assessors documented 39.6% surgical site infection prevalence 5 to 7 days after surgery, confirming clinical need, relevance, reliability, and feasibility of using this web-based tool to standardise community surgical site infection surveillance, noting that it was user-friendly, more efficient to use than traditional paper-based tools and useful as a registry for tracking progress.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".