Good or best practice statements: proposal for the operationalisation and implementation of GRADE guidance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An evidence-based approach is considered the gold standard for health decision-making. Sometimes, a guideline panel might judge the certainty that the desirable effects of an intervention clearly outweigh its undesirable effects as high, but the body of supportive evidence is indirect. In such cases, the application of the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach for grading the strength of recommendations is inappropriate. Instead, the GRADE Working Group has recommended developing ungraded best or good practice statement (GPS) and developed guidance under which circumsances they would be appropriate.Through an evaluation of COVID-1- related recommendations on the eCOVID Recommendation Map (COVID-19.recmap.org), we found that recommendations qualifying a GPS were widespread. However, guideline developers failed to label them as GPS or transparently report justifications for their development. We identified ways to improve and facilitate the operationalisation and implementation of the GRADE guidance for GPS.Herein, we propose a structured process for the development of GPSs that includes applying a sequential order for the GRADE guidance for developing GPS. This operationalisation considers relevant evidence-to-decision criteria when assessing the net consequences of implementing the statement, and reporting information supporting judgments for each criterion. We also propose a standardised table to facilitate the identification of GPS and reporting of their development. This operationalised guidance, if endorsed by guideline developers, may palliate some of the shortcomings identified. Our proposal may also inform future updates of the GRADE guidance for GPS.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it