Marcos GRADE de la evidencia a la decisión (EtD): un enfoque sistemático y transparente para tomar decisiones sanitarias bien informadas. 2: Guías de práctica clínica
Bibliographic record
Abstract
Clinicians do not have the time or resources to consider the underlying evidence for the myriad decisions they must make each day and, as a consequence, rely on recommendations from clinical practice guidelines. Guideline panels should consider all the relevant factors (criteria) that influence a decision or recommendation in a structured, explicit, and transparent way and provide clinicians with clear and actionable recommendations. In this article, we will describe the Evidence to Decision (EtD) frameworks for clinical practice recommendations. The general structure of the EtD framework for clinical recommendations is similar to EtD frameworks for other types of recommendations and decisions, and includes formulation of the question, an assessment of the different criteria, and conclusions. Clinical recommendations require considering criteria differently, depending on whether an individual patient or a population perspective is taken. For example, from an individual patient's perspective, out-of-pocket costs are an important consideration, whereas, from a population perspective, resource use (not only out-of-pocket costs) and cost effectiveness are important. From a population perspective, equity, acceptability, and feasibility are also important considerations, whereas the importance of these criteria is often limited from an individual patient perspective. Specific subgroups for which different recommendations may be required should be clearly identified and considered in relation to each criterion because judgments might vary across subgroups. This article is a translation of the original article published in the 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 | 0.270 | 0.643 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".