Incidence of surgical site infections after caesarean sections in a community hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Rates of performed caesarean sections have increased globally [1]. Surgical site infections (SSIs) following a caesarean section pose a threat to the safety of the patient. This study intended to determine the current SSI rate after caesarean sections at one community hospital. The rate of incidence of these infections was compared to benchmark rates from various studies, including a report from the American National Healthcare Safety Network (NHSN). This comparative study provides objective evidence of performance in relation to SSIs. Method: The primary data collection method included a form completed by the obstetrician-gynecologists of the individual patients at the six-week post-partum follow-up visit. Demographic data was collected retrospectively through analysis of medical records. Patients who underwent a caesarean section in the seven-month data collection period (between November 2015 and May 2016) were asked to participate, and consent was obtained. Results: A total of 118 caesarean sections were reviewed and seven SSIs diagnosed. A crude SSI rate was calculated at 5.9%. For further insight, NHSN risk-adjusted SSI rates were calculated. The NHSN risk-adjusted SSI rate was determined at 6.1% for those patients presenting with a risk index level of 0 and at 5.9% for those with a risk index level of 1. Both NHSN risk index levels of 2 and 3 were identified to have an adjusted SSI rate of 0.0%. Conclusion: This study, while limited in scope, does add to the collective literature on SSI rates following caesarean sections. Most significantly, it provides a methodology for other centres interested in determining their own infection rates and could lead to improved practices and better patient outcomes.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it