Surgical count process for prevention of retained surgical items: an integrative review
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
AIMS AND OBJECTIVES: To analyse the evidence reported in the literature concerning the surgical count process for surgical sponges, surgical instruments and sharps and to identify knowledge gaps for future research on the surgical count process. BACKGROUND: The surgical count process stands out among the practices advocated by the World Health Organization to ensure surgical safety. The literature indicates that this practice should be performed in all surgical processes. However, surgical items are still retained. DESIGN: Integrative review. METHODS: The literature search was conducted in the PubMed, CINAHL and LILACS databases and included studies on the surgical count process published in English, Spanish and Portuguese from January 2003-December 2013. RESULTS: A total of 28 primary studies were included in the sample, allowing the knowledge on the surgical count process to be summarised and grouped into three categories: risk factors for retained surgical items, how the surgical count process should be performed in the intraoperative period and the accompanying technologies that collaborate to improving the manual count process. CONCLUSIONS: The correct implementation of the surgical count process by the perioperative nurse may contribute to preventing retained surgical items, thereby improving surgical patient safety. RELEVANCE TO CLINICAL PRACTICE: Nurses can use this review to assist in decision-making directed towards preparing, updating and implementing a reliable system for the surgical count process based on recent evidence because the perioperative nurse plays a key role in the implementation of this practice in health services.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it