Systems for grading the quality of evidence and the strength of recommendations II: Pilot study of a new system
Bibliographic record
Abstract
BACKGROUND: Systems that are used by different organisations to grade the quality of evidence and the strength of recommendations vary. They have different strengths and weaknesses. The GRADE Working Group has developed an approach that addresses key shortcomings in these systems. The aim of this study was to pilot test and further develop the GRADE approach to grading evidence and recommendations. METHODS: A GRADE evidence profile consists of two tables: a quality assessment and a summary of findings. Twelve evidence profiles were used in this pilot study. Each evidence profile was made based on information available in a systematic review. Seventeen people were given instructions and independently graded the level of evidence and strength of recommendation for each of the 12 evidence profiles. For each example judgements were collected, summarised and discussed in the group with the aim of improving the proposed grading system. Kappas were calculated as a measure of chance-corrected agreement for the quality of evidence for each outcome for each of the twelve evidence profiles. The seventeen judges were also asked about the ease of understanding and the sensibility of the approach. All of the judgements were recorded and disagreements discussed. RESULTS: There was a varied amount of agreement on the quality of evidence for the outcomes relating to each of the twelve questions (kappa coefficients for agreement beyond chance ranged from 0 to 0.82). However, there was fair agreement about the relative importance of each outcome. There was poor agreement about the balance of benefits and harms and recommendations. Most of the disagreements were easily resolved through discussion. In general we found the GRADE approach to be clear, understandable and sensible. Some modifications were made in the approach and it was agreed that more information was needed in the evidence profiles. CONCLUSION: Judgements about evidence and recommendations are complex. Some subjectivity, especially regarding recommendations, is unavoidable. We believe our system for guiding these complex judgements appropriately balances the need for simplicity with the need for full and transparent consideration of all important issues.
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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 | 0.451 | 0.677 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".