Surgical site infection rates at the Pontiac Health Care Centre, a rural community hospital.
Bibliographic record
Abstract
INTRODUCTION: The prevalence of surgical site infections (SSIs) at the Pontiac Health Care Centre, a rural hospital, was compared with rates obtained by large multicentre studies. Postoperative nosocomial infection (NI) rates were also calculated. METHODS: A review of all surgical interventions involving an incision, excluding ophthalmological procedures, performed between October 2001 and March 2003 (n = 831) was undertaken. Various clinical parameters were recorded. Infection rates were calculated. Data were analyzed using either the chi2 or Student's t test. RESULTS: The overall SSI rate was 5.54%: 3.50% in clean cases (C), 6.77% in clean-contaminated cases (CC), and 14.58% in contaminated or dirty cases (D). The postoperative NI rate was 6.62% (C, 3.68%; CC, 9.90%; D, 16.67%). The mean duration of surgery was significantly higher among patients with SSIs and with NIs than those without infections for CC (133 +/- 95 v. 78 +/- 60 min, p < 0.05, and 129 +/- 82 v. 77 +/- 60 min, p < 0.001 respectively) and D (130 +/- 96 v. 82 +/- 62 min, p < 0.001, and 136 +/- 92 v. 80 +/- 60 min, p < 0.001 respectively). There were significantly higher SSI and NI rates among patients with combined American Society of Anesthesiologists (ASA) scores II and III than those with ASA score I in D (chi2 = 5.06 and chi2 = 6.34 respectively). There was also significantly higher SSI and NI rates among patients with combined Comorbidity Scale score 1-6 than those with no comorbid factors in CC (chi2 = 4.14 and chi2 = 4.42 repectively) and D (not significant and chi2 = 4.04 respectively). CONCLUSION: SSI rates at the Pontiac Health Care Centre were comparable to multicentre rates. Wound contamination category, type of surgery, duration of surgery, ASA score and Comorbidity Scale score were associated with SSI and NI rates. Studies have shown that examining NI rates decreases these rates by raising awareness; thus, we suggest that rural hospitals implement protocols to survey their postoperative NI rates.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".