Basing Clinical Decision on GRADE recommendations: Implications for Practice and Lessons Learned about Evidence
Bibliographic record
Abstract
The Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework was developed as an evidence based approach to translating research evidence to clinical practice through recommendations. GRADE has become widely adopted by key healthcare stakeholders, and thus, recommendations derived using the framework have important implications on the care people receive, worldwide. The main activities of GRADE are: 1) assessing the quality/certainty of evidence, and 2) making a recommendation about how to manage the care of a patient with a specified disease or health condition. The assessment of evidence quality/certainty is based on a hierarchy of evidence, similar to that made popular by the Evidence Based Medicine (EBM) movement. In making a recommendation, users of the framework are to consider several factors, including quality of evidence, resource implications, and values and preferences of “typical” patients. Recommendations carry with them instruction on how to engage the patient in the clinical encounter. Despite its popularity and several modifications to the framework, GRADE has received little scrutiny regarding its structure and usefulness. When examined, several problems for patient care in the clinical encounter become apparent, stemming from a lack of a clear and justified theoretical and empirical basis. These problems pertain to GRADE’s narrow view on evidence, emphasis on average effects (both therapeutic effect estimates and patient “values and preferences”) derived from populations, and apparent promotion of paternalism. The result is one cannot know whether the use of GRADE is helpful or harmful for patient care. An examination of GRADE reveals several lessons for clinical decision making. First, judgment is unavoidable – even the framework relies on judgment of its users in assessing the quality and relevance of information as evidence and weighing that with other considerations. Second, a wide view on evidence may be necessary to understand what will help the patient. Finally, knowledge generation and its use for clinical practice is ultimately a social activity, one that is best facilitated through a process of deliberation. Thus, I put forward the thesis that clinical decisions require judgment, a broad view on evidence, and deliberation, something that cannot be replaced by a procedural framework.
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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 | 0.408 | 0.843 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.037 | 0.030 |
| Open science | 0.019 | 0.012 |
| Research integrity | 0.026 | 0.032 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".