Late and Persistent Symptoms Suggestive of Surgical Site Infections After Cesarean Section: Results from a Prospective Cohort Study in Rural Rwanda
Bibliographic record
Abstract
Background:Women in low-resource settings will likely develop late surgical site infections (SSIs), diagnosed after post-operative day (POD) 10. We measured SSI prevalence and predictors of late and persistent SSIs-suggestive symptoms among women who delivered via cesarean section (c-section). Patients and Methods:Women who underwent c-sections at Kirehe District Hospital (KDH) between September 2019 and February 2020 were prospectively enrolled. Data were collected on POD1, POD11, and POD30. Logistic regression identified factors associated with persistent and late SSI symptoms. Results:In total, 808 women were study enrolled. Of these, 646 women physically attended the POD11 clinic visit follow-up, while 671 received the POD30 telephone-based follow-up review. Thirty-three (5.0%) women were diagnosed with an SSI on POD11, and 39 (5.3%) had an SSI diagnosis during POD11 to POD30, giving a cumulative prevalence of 10.3% late SSI rate. Of 671, 400 (59.9%) reported at least one SSI-associated symptom between POD11 and POD30. The reported symptoms included pain (56.6%), fever (19.4%), or incision drainage (16.6%). Of these, 200 women reported still having at least one of these symptoms on POD30. Of the 400 women with late SSI symptoms, 232 (58.0%) did not seek care, and of these, 80 (48.5%), 59 (35.8%), and 15 (8.9%) could not afford transport fare, did not believe symptoms were severe for a medical visit, and were not able to travel, respectively. Lower odds of late SSI-suggestive symptoms were reported among women with health insurance (adjusted odds ratio [aOR], 0.06; p = 0.013), whereas higher late SSI-suggestive symptoms odds were among women with wealthier socioeconomic status (aOR, 2.88; p = 0.004). Conclusions:Women in rural Rwanda are at risk of late and persistent SSI-suggestive symptoms. Financial barriers and the perception that their symptoms were not serious enough for the medical visit need education on early care seeking and interventions to mitigate financial barriers for optimizing perinatal care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".