El sistema GRADE: un cambio en la forma de evaluar la calidad de la evidencia y la fuerza de recomendaciones
Bibliographic record
Abstract
Individual clinicians and organizations making health care decisions should not only consider the magnitude of the benefits and harms of different courses of action (interventions), but also the confidence we can have in those estimates. The Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach offers a systematic and transparent way to summarize the evidence, to rate the confidence we can have in the effects of the interventions and to move from evidence to recommendations. The GRADE approach has been adopted by several organizations worldwide, including the World Health Organization and the Cochrane Collaboration. In Chile, this approach has already been used by guidelines produced by the Chilean Ministry of Health. In this paper we describe the core concepts of the GRADE approach to rate the quality of the evidence and to grade the strength of recommendations. As clinicians, being familiar with such concepts may be helpful to make decisions informed by the best available evidence.
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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 | 0.441 | 0.728 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".