P056 The Canadian Task Force on Preventive Health Care: Interpretation tool to compare previous grading of recommendations to GRADE
Bibliographic record
Abstract
Background Guideline producers use various methods to grade their recommendations. The Canadian Task Force on Preventive Health Care (CTFPHC) previously assigned letter grades to their recommendations based on an evaluation of the evidence considering only study design. The CTFPHC now uses GRADE (Grading of Recommendations Assessment, Development and Evaluation), which considers the quality of the evidence and the strength of the recommendations. Objectives The objectives were to develop a tool that would allow interpretation of the previous CTFPHC grading system to GRADE to enable comparison of guidelines. Methods A comparison and mapping of each level of evidence and strength of recommendation for each grading system was undertaken. The methods working group and the knowledge translation working group of the CTFPHC reviewed the mapping system, which was then tested with a previous CTFPHC recommendation from 2001. Results An interpretation tool which maps the level of evidence and the strength of recommendation was created and applied to a previous CTFPHC recommendation statement. Implications for Guideline Developers/Users The tool is not meant to be a perfect system and does not require a formal assessment of the evidence from former guidelines. The evidence from previous guidelines is not assessed using GRADE, but rather interpreted for GRADE language. This tool will be useful in helping guideline developers compare guidelines using different grading systems and will allow the CTFPHC to compare previous guidelines to GRADE. This tool may also be applied when critically appraising guidelines from other guideline development groups.
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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 | 0.222 | 0.707 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.041 | 0.034 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".