Marcos GRADE de la evidencia a la decisión (EtD): un enfoque sistemático y transparente para tomar decisiones sanitarias bien informadas. 1: Introducción
Bibliographic record
Abstract
Los médicos y quienes elaboran guías y políticas a veces pasan por alto criterios importantes, les dan un peso indebido o no usan la mejor evidencia disponible para informar sus juicios. Los sistemas explícitos y transparentes para la toma de decisiones pueden ayudar a garantizar que se consideren todos los criterios importantes, y que las decisiones estén basadas en la mejor evidencia disponible. El grupo de trabajo GRADE ha desarrollado marcos «de la evidencia a la decisión» (EtD) para los diferentes tipos de recomendaciones o decisiones. El objetivo de los marcos EtD es ayudar a los paneles a usar la evidencia de una manera estructurada y transparente para informar las decisiones respecto de las recomendaciones clínicas, decisiones de cobertura sanitaria y recomendaciones o decisiones sobre el sistema sanitario o sobre salud pública. Los marcos EtD tienen una estructura común: formulación de una pregunta, evaluación de la evidencia y conclusiones. No obstante, existen diferencias entre los marcos para cada tipo de decisión. Los marcos EtD informan a los usuarios sobre los juicios que se han hecho y la evidencia que los apoya dotando de transparencia la base para las decisiones de los que tienen que tomarlas. Los marcos EtD también facilitan la diseminación de las recomendaciones y permiten a los decisores de otros ámbitos adoptar recomendaciones o decisiones, o adaptarlas a su contexto. El siguiente artículo es una traducción del artículo original publicado en British Medical Journal. Los marcos EtD se utilizan actualmente en el marco del Programa de Guías de Práctica Clínica en el Sistema Nacional de Salud, coordinado por GuíaSalud. Clinicians, guideline developers, and policymakers sometimes neglect important criteria, give undue weight to criteria, and do not use the best available evidence to inform their judgments. Explicit and transparent systems for decision making can help to ensure that all important criteria are considered and that decisions are informed by the best available research evidence. The GRADE Working Group has developed Evidence to Decision (EtD) frameworks for the different type of recommendations or decisions. The purpose of EtD frameworks is to help people use evidence in a structured and transparent way to inform decisions in the context of clinical recommendations, coverage decisions, and health system or public health recommendations and decisions. EtD frameworks have a common structure that includes formulation of the question, an assessment of the evidence, and drawing conclusions, though there are some differences between frameworks for each type of decision. EtD frameworks inform users about the judgments that were made and the evidence supporting those judgments by making the basis for decisions transparent to target audiences. EtD frameworks also facilitate dissemination of recommendations and enable decision makers in other jurisdictions to adopt recommendations or decisions, or adapt them to their context. This article is a translation of the original article published in British Medical Journal. The EtD frameworks are currently used in the Clinical Practice Guideline Programme of the Spanish National Health System, co-ordinated by GuíaSalud.
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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 | 0.137 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".