La trayectoria metodológica de la evaluación de la eficiencia y su futuro
Bibliographic record
Abstract
espanolLas nuevas intervenciones sanitarias tienen que demostrar su eficiencia a traves de los estudios de eficiencia o evaluaciones economicas. En la actualidad, Australia, Canada, Reino Unido, Escocia, Belgica, Holanda, Suecia, Noruega, Finlandia, Portugal y Hungria emplean formalmente el criterio de coste-efectividad para tomar decisiones en politica farmaceutica. El diseno mas empleado para su realizacion son los modelos analiticos de decision, aunque los estudios clinicos, los registros de pacientes, y la historia clinica electronica cada vez se emplean mas. Los analisis coste-efectividad son los mas usados, utilizando los anos de vida ganados y los anos de vida ajustados por calidad e incluyendo los costes sanitarios y, cuando la perspectiva elegida es la sociedad, tambien costes no sanitarios y costes indirectos. Para analizar los resultados se calcula el cociente coste/efectividad (o utilidad) incremental, realizando siempre un analisis de sensibilidad, que puede ser univariante y/o probabilistico. Es necesario mejorar aspectos metodologicos en las evaluaciones economicas para que cada vez sean mas validas y transparentes, asi como definir su papel en los analisis de decision multicriterio, en los estudios con datos de vida real, en la evaluacion de la inmunooncologia y en la medicina personalizada de precision. EnglishNew health interventions have to demonstrate their efficiency through efficiency studies or economic evaluations. Currently, Australia, Canada, the United Kingdom, Scotland, Belgium, the Netherlands, Sweden, Norway, Finland, Portugal and Hungary formally use the cost-effectiveness criteria to make decisions in pharmaceutical policy. The decision analytical models are the most commonly used design, although clinical studies, patient registries, and electronic medical records are increasingly used. The cost-effectiveness analysis are the most used, using the years of life gained and the qualityadjusted life years and including health costs and, when the chosen perspective is society, also non-health costs and indirect costs. To analyze the results, the incremental cost/effectiveness ratio (or cost/utility ratio) is calculated, always carrying out a sensitivity analysis, which can be univariate and/ or probabilistic. It is necessary to improve methodological aspects in economic evaluations so that they become increasingly valid and transparent, as well as defining their role in multi-criteria decision analysis, in studies with real-life data, in the evaluation of immuno-oncology and in personalized precision medicine.
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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 | 0.124 | 0.222 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".