Implantación de buenas prácticas en el cuidado y mantenimiento de los dispositivos de accesos vasculares: estrategia y primeros resultados
Bibliographic record
Abstract
Objectives: To describe the strategy of implementation in clinical practice of the Good Practices Guide "Care and Maintenance to reduce vascular access complications" of the RNAO (Registered Nurses Association of Ontario) in four units at the Regional University Hospital of Malaga, and to present the first clinical results associated with the use of vascular access devices (VAD). Methodology: The strategy was developed through different activities: implantation teams were created composed of leading care nurses of the UGC, trained in the evidence implantation model (model CCEC®/BPSO®) and in the management of the VAD, work procedures were updated, recommendations were disseminated and new records were designed and implanted in the Diraya care station to monitor the results associated with the use of VAD. Results: Posters have been designed with recommendations and new records. Regarding the clinical results, a high adherence (98-99%) to the recommendations of evidence-based care in the management of VADs is observed: hand hygiene, device selection, patient safety barrier measures, and an incidence of phlebitis of the DAV (2.89%) like to other published studies. Conclusion: The implementation of the recommendations of this guide has fostered innovation and research in care, the training of professionals, the implementation of good practices based on evidence and the ability to have information on health outcomes associated with use of DAV.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".