[GRADE: from grading the evidence to developing recommendations. A description of the system and a proposal regarding the transferability of the results of clinical research to clinical practice].
Bibliographic record
Abstract
The Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group represents an international collaboration of guideline developers, clinicians, health services researchers and methodologists. Many leading organizations, including the World Health Organization (WHO), use the GRADE approach because it has led to progress in the assessment of evidence and in the development of healthcare recommendations. The GRADE system distinguishes the quality of evidence from the strength of a recommendation. The quality of evidence reflects the extent of confidence that an estimate of effect is correct if it is used in the context of single endpoints. In the context of giving guidance, it reflects the extent to which confidence in an estimate of the effect is adequate to support recommendations. The strength of a recommendation, separated into strong and weak or conditional recommendations for or against an intervention, is defined as the extent to which one can be confident that the desirable effects of an intervention outweigh the undesirable effects. A recommendation for action requires consideration for the magnitude of the expected benefit and downsides of an intervention for all patient-important endpoints, the associate values and preferences and resource use. The GRADE system includes a systematic approach to evaluate the generalizability of study results to healthcare practice. Judgments about generalizability, better termed directness, are separated into judgments about the availability of direct comparisons between two alternative management strategies and judgments about differences between the population, intervention, comparator to the intervention, and outcomes (PICO) of interest for a given question, and those included in the relevant studies. In addition to providing an overview of the GRADE system, this article focuses on the approach to assessing directness or generalizability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.085 | 0.292 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.031 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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".