Modelo artificial de sutura quirúrgica Morales Meseguer. Resultados preliminares
Bibliographic record
Abstract
[[SPA] Durante la realización de las prácticas de cirugía, nuestros alumnos deben de familiarizarse con la ejecución de suturas quirúrgicas. Una oportunidad de realizar el entrenamiento previo a la sutura de un paciente es este modelo artificial, en el que aúna su parecido estructural con la piel humana, el bajo coste del modelo artificial propuesto, el ser un modelo fácilmente reproducible y la gran utilidad que le supone al estudiante de medicina para el aprendizaje de la realización de una correcta sutura quirúrgica. Los resultados preliminares se derivan de una muestra de 28 estudiantes que realizaron una encuesta anónima online tipo Likert con los siguientes ítems: sencillez del modelo, comodidad, aprendizaje y seguridad proporcionada ante la realización de una sutura en un paciente real. [ENG] While performing surgery practices, our students should be familiar with the performance of surgical sutures. An opportunity for training prior to suture a patient is this artificial model, which combines its structural resemblance to human skin, the low cost of the proposed artificial model, being an easily reproducible and useful model to supposed to medical student learning of performing proper surgical suture. Preliminary results are derived from a sample of 28 students who completed an anonymous online survey with Likert type the following items: model simplicity, convenience, and safety training provided to the embodiment of a suture on a real patient
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".