Los modelos de simulación de eventos discretos en la evaluación económica de tecnologías y productos sanitarios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
El uso de modelos matemáticos como instrumentos de evaluación de alternativas está teniendo una importancia cada vez mayor en el terreno de la evaluación económica de servicios y tecnologías sanitarias, con un papel cada vez más relevante como ayuda en la toma de decisiones en la gestión sanitaria. Hasta ahora se han usado fundamentalmente 2 tipos de modelos, en parte en función de la enfermedad estudiada. De esta forma, los árboles de decisión han sido muy utilizados para las enfermedades de carácter agudo y los modelos de Markov han sido usados en enfermedades crónicas o que presentan estados de salud recurrentes. Sin embargo, tanto unos como otros presentan importantes limitaciones a la hora de modelar de forma realista ciertos procesos o enfermedades, y por ello está creciendo el interés y el uso de los modelos de simulación de eventos discretos. El objetivo del presente artículo es describir las principales características que presentan los modelos de simulación de eventos discretos, describir las últimas novedades, así como presentar qué ventajas aportan con respecto a los otros tipos de modelos en economía de la salud y, especialmente, en la evaluación económica de tecnologías y productos sanitarios. The use of mathematical models to assess therapeutic alternatives is increasing in the economic evaluation of health technologies and services and these models are becoming an increasingly important aid to decision making in health care. Until now, 2 types of model have been used, depending to some extent on the disease to be studied: decision trees have been used for acute diseases and Markov models in chronic or recurrent diseases. However, both models present major limitations when addressing complex processes or diseases. Consequently, interest in, and the use of, discrete-event simulation is growing. The present article aims to describe the main characteristics of discrete-event simulation, the state of the art in this field, and the advantages of these models with respect to other kinds of models in health economics, especially in the evaluation of health technologies and product assessment.
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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 | 0.023 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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 it