Surgical Site Infection Rates After Implementation of the Surgical Care Improvement Project Initiative
Bibliographic record
Abstract
Surgical site infections (SSIs) reflect a serious complication in modern healthcare and are a substantial burden to healthcare systems and service payers worldwide in terms of patient morbidity, mortality, and additional costs. The Surgical Care Improvement Project (SCIP), introduced in 2006, was developed by the Centers for Medicare and Medicaid Services to reduce SSI rates by 25%. However, SCIP was retired in 2015. Given the considerable financial burden of SSIs and because SSIs may be prevented using evidence-based measures, it was worth revisiting and re-evaluating the quality improvement efforts brought about by the success of the evidence-based SCIP initiative. This project aimed to examine the relationship between SCIP infection-prevention process-of-care measures and SSI rates between the years of high SCIP compliance, and several years after it was retired. The nature of this doctoral project was a quality improvement evaluation via a retrospective review of medical records acquired from the first quarter of 2014 to the fourth quarter of 2018. The SCIP core measure guidelines were used to define standards for care and thresholds for adherence. SSI rates were extracted and aggregated to look at trends and the chi-square test was used to show the relationship between two categorical variables. The analysis showed a significant difference between the proportions of infections from those of high SCIP compliance compared to the years following SCIP retirement (SCIP (Χ2(2) = 11.12, p < .004). The improvement of individual, community, and societal health is a significant contribution made by the nursing profession. The concept of SSI is essential in building the nursing science that will lead to identifying sound nursing interventions in the perioperative period.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".