Improving quality and patient safety in surgical care through standardisation and harmonisation of perioperative care (SAFEST project): A research protocol for a mixed methods study
Bibliographic record
Abstract
INTRODUCTION: Adverse events in health care affect 8% to 12% of patients admitted to hospitals in the European Union (EU), with surgical adverse events being the most common types reported. AIM: SAFEST project aims to enhance perioperative care quality and patient safety by establishing and implementing widely supported evidence-based perioperative patient safety practices to reduce surgical adverse events. METHODS: We will conduct a mixed-methods hybrid type III implementation study supporting the development and adoption of evidence-based practices through a Quality Improvement Learning Collaborative (QILC) in co-creation with stakeholders. The project will be conducted in 10 hospitals and related healthcare facilities of 5 European countries. We will assess the level of adherence to the standardised practices, as well as surgical complications incidence, patient-reported outcomes, contextual factors influencing the implementation of the patient safety practices, and sustainability. The project will consist of six components: 1) Development of patient safety standardised practices in perioperative care; 2) Guided self-evaluation of the standardised practices; 3) Identification of priorities and actions plans; 4) Implementation of a QILC strategy; 5) Evaluation of the strategy effectiveness; 6) Patient empowerment for patient safety. Sustainability of the project will be ensured by systematic assessment of sustainability factors and business plans. Towards the end of the project, a call for participation will be launched to allow other hospitals to conduct the self-evaluation of the standardized practices. DISCUSSION: The SAFEST project will promote patient safety standardized practices in the continuum of care for adult patients undergoing surgery. This project will result in a broad implementation of evidence-based practices for perioperative care, spanning from the care provided before hospital admission to post-operative recovery at home or outpatient facilities. Different implementation challenges will be faced in the application of the evidence-based practices, which will be mitigated by developing context-specific implementation strategies. Results will be disseminated in peer-reviewed publications and will be available in an online platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.151 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".