Core GRADE 2: choosing the target of certainty rating and assessing imprecision
Bibliographic record
Abstract
This second article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach to deciding on the target of the certainty rating, and decisions about rating down certainty of evidence due to imprecision. Core GRADE users assess if the true underlying treatment effect is important or not in relation to the minimal important difference (MID) or, alternatively, if a true underlying treatment effect exists. The location of the point estimate of effect in relation to the chosen threshold determines the target. For instance, using the MID thresholds, a point estimate greater than the MID suggests an important effect and less than the MID, an unimportant or little to no effect. Users then rate down for imprecision if the 95% confidence interval crosses the MID for benefit or harm.
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 | 0.332 | 0.746 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".