Surgical count process for prevention of retained surgical items: an integrative review
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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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".