A Quality Improvement Study Project to Improve Post Cesarean Section Surgical Site Infection Surveillance in a District Hospital in Kigali City
Bibliographic record
Abstract
Post-caesarean surgical site infection (PCSI) is one of the most common cesarean section-related complications. In low- and middle-income countries (LMIC), PCSI prevalence is often under-reported and inaccurate because LMIC surveillance systems are often unable to detect PCSIs developed after discharge; this can ultimately wrongly inform the decision-making related to reducing PCSIs.This paper describes the establishment of a post-discharge PCSI surveillance system for identification of PCSI rate in a district hospital in Rwanda.A total of 540 women underwent CS in the hospital from November 2017 to February 2018, and 536 (99.3%) consented to participate in the surveillance. Among those consented, 22 had no telephone and 174 could not be reached by telephone despite multiple attempts. At the end of this study, a total of 340 women completed the entire surveillance period. The total PCSI rate was 11.5%.Out of all PCSIs, 21% were detected during hospitalization period and 79% were detected during the post-discharge period.The PCSI surveillance system developed in this project covered the 30-day period after surgery and provided a more accurate estimate of PCSI rate. The system was able to track PCSIs developed after a patient was discharged from the hospital. Long term sustainability of the project must be evaluated.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".