Assessing Changes in Surgical Site Infections and Antibiotic Use among Caesarean Section and Herniorrhaphy Patients at a Regional Hospital in Sierra Leone Following Operational Research in 2021
Bibliographic record
Abstract
Surgical site infections (SSIs) are a major public health threat to the success of surgery. This study assessed changes in SSIs and use of antibiotics among caesarean section (CS) and herniorrhaphy patients at a regional hospital in Sierra Leone following operational research. This was a comparative before and after study using routine hospital data. The study included all the CS and herniorrhaphy patients who underwent surgery between two time periods. Of the seven recommendations made in the first study, only one concerning improving the hospital’s records and information system was fully implemented. Three were partially implemented and three were not implemented. The study population in both studies showed similar socio-demographic characteristics. The use of postoperative antibiotics for herniorrhaphy in both studies remained the same, although a significant increase was found for both pre- and postoperative antibiotic use in the CS patients, 589/596 (98.8%) in 2023 and 417/599 (69.6%) in 2021 (p < 0.001). However, a significant decrease was observed in the overall incidence of SSIs, 22/777 (2.8%) in 2023 and 46/681 (6.7%) in 2021 (p < 0.001), and the incidence of SSIs among the CS patients, 15/596 (2.5%) in 2023 and 45/599 (7.5%) in 2021 (p < 0.001). The second study highlights the potential value of timely assessment of the implementation of recommendations following operational research.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".