Relationship Between Characteristics and Risk Factors in Postoperative Surgical Wound Infections in Cesarean Section Patients
Bibliographic record
Abstract
Background: Cesarean section rates have increased globally, with Indonesia showing a rise from 6.5% in 2016 to 16.5% in 2021. Although cesarean deliveries reduce certain risks, they also present the risk of surgical site infections (SSIs), a leading cause of postpartum infections. This study explores the relationship between patient characteristics and SSI following cesarean sections at Hasan Sadikin General Hospital, Bandung. Methods: This study used an analytical observational design with a retrospective case-control approach. Data were collected from the medical records of patients who underwent cesarean sections from January 2022 to January 2023. The sample consisted of 23 cases of patients with SSI and 46 controls. The analysis was performed using the Chi-square test and multivariate logistic regression. Results: Body mass index (BMI) and urinary tract infection (UTI) during pregnancy were found to be significant risk factors for SSI, with P-values of 0.019 and 0.001, respectively. UTI is the most significant risk factor with an adjusted odds ratio (aOR) of 6.48 (95% confidence interval (CI) 1.7 - 64.5). Overweight and obesity also showed a higher risk, with an aOR of 10.1 (95% CI 1.6 - 64) and 4.07 (95% CI 1.0 - 18.4), respectively. Other characteristics such as age, parity, history of anemia, diabetes mellitus, preeclampsia, premature rupture of membranes, previous cesarean section, and the type of cesarean incision were not significantly associated with SSI. Conclusion: Obesity and UTI during pregnancy are significant risk factors for SSI in post-cesarean section patients. Strict monitoring and preventive interventions for these conditions during pregnancy should be implemented to reduce SSI incidence and improve maternal and infant health after cesarean section.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".