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 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".