Conclusiveness resolves the conflict between quality of evidence and imprecision in GRADE
Bibliographic record
Abstract
OBJECTIVES: The objective of our article is to show how "quality of evidence" and "imprecision," as they are defined in Grading of Recommendations Assessment, Development, and Evaluation (GRADE) articles, may lead to confusion. We focus only on the context of systematic reviews. STUDY DESIGN AND SETTING: We analyze, with the aid of standard probabilistic and statistical concepts, the concepts of quality of evidence and imprecision as used in the GRADE framework. This enables us to point out some weaknesses in the relation between "quality of evidence" and "imprecision." RESULTS: The GRADE framework contains terms familiar from classical statistics, but these terms are used in nonstandard ways. Notably, "imprecision" does not have the meaning in the GRADE framework that it has in statistics, and the well-known table of "evidence levels" wrongly suggests that "quality of evidence" and "accuracy" express the same concept-they do not. CONCLUSION: We believe that "conclusiveness" rather than "imprecision" would be a suitable term to use when the question whether the CI excludes or includes certain critical margins is being addressed. Conclusiveness could also replace quality of evidence as the final step for a systematic reviewer.
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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 | 0.572 | 0.869 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".