Alcance de la implantación de la guía “valoración del riesgo y prevención de úlceras por presión de la 'Registered Nurses’ Association of Ontario (RNAO)
Bibliographic record
Abstract
Background: The Best Practice Spotlight \nOrganizations® Program is being developed in \nSpain to reduce the variability of clinical practice \nby implementing clinical practice guidelines from \nthe Registered Nurses’ Association of Ontario. This \nstudy described the results of the implementation \nof the guide “Risk assessment and prevention of \npressure ulcers”. \nMethods: We carried out a retrospective observational \nstudy (2015-2018) at the Hospital \nUniversitario Virgen de las Nieves on 4,464 patients \nfrom 22 hospitalization units, analyzing type \nof unit, risk assessment, preventive measures, origin \nand category of ulcers. Descriptive analysis and \ncontingency tables were performed with the Chisquare \nstatistic p<0.05. \nResults: The patients at risk were 62.2% in medical \nunits, 53.4% in surgical units and 90% in intensive \ncare. The application of preventive measures \nwas 67.9%, 60.2% and 92.1% (respectively) for \neach unit. In medical units, 13.1% of pressure ulcers \nwere identified, of which 68.1% were present \nat the time of admission. While in surgical units \nand intensive care they developed during hospitalization \n(60.8% and 88.9% respectively) (p<0.001). \nThe presence of ulcers seemed to show a decreasing \ntrend in the years analyzed (19.6% to 11.2%). \nConclusions: There are favorable environments \nfor implantation (medical units and intensive \ncare) that reflect a higher level of risk assessment, \nuse of pressure management surfaces and a decrease \nin prevalence. The recommendations have not \nbeen implemented homogeneously, with differences \ndepending on the type of unit.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".