Improved Accuracy of Caesarean Section Surgical Site Infection Surveillance Utilizing Post-Discharge Email
Bibliographic record
Abstract
Background. Post-discharge surveillance for surgical site infections (SSI) is critical for procedures with short hospital stays. We describe SSI rates for Caesarean sections (C/S) with six years of surveillance data using conventional and innovative approaches to post-discharge surveillance. Methods. At an acute care hospital in Vancouver, Canada, C/S SSI surveillance was routinely performed from 1 April 2009 to 31 March 2015. A 30-day, post-discharge component was implemented starting 1 April 2012. In the first year of post-discharge surveillance, patient self-reported infections via traditional mail surveys; in the second year, an Infection Control Practitioner (ICP) initiated phone calls to patients (in addition to mail surveys); in the third year, electronic follow-up via email was initiated. We examined SSI rates, response rates and time spent by an ICP engaging with patients for responses. The χ2 tests were applied to comparisons of the three years with and without post-discharge surveillance. Results. In the first three years, 1939 C/S and in the final three years, 1947 C/S were performed, respectively. The SSI rate in the first three years was 0.36 per 100 procedures (6 superficial and 1 deep). In the final three years, the SSI rate was 1.39 per 100 procedures (19 superficial, 1 deep, 7 organ space). Significantly more infections were captured (p < 0.001) and a significantly higher number of organ space and deep incisional infections were identified (p = 0.020) with post-discharge surveillance. Response rates improved significantly, from 20% to 46% with mail and phone calls. Response rates with email were 73% email (p < 0.001). Time spent by ICP engaging with patients for responses for post-discharge surveillance varied from 24.9 hours in 2012/2013 (711 C/S) to 40.5 hours in 2013/2014 (654 C/S) to 35.7 hours in 2014/2015 (582 C/S) with the corresponding response rates of 20%, 46% and 73%. Conclusion. Post-discharge surveillance for patients who undergo C/S detected significantly more SSI, including deep incisional and organ space infections. Email follow-up was an efficient and effective method of post-discharge surveillance and should be included routinely in a SSI surveillance program for procedures associated with short hospital lengths of stay. Disclosures. All authors: No reported disclosures.
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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 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".