Protocol for the Creation of the Guidelines International Network–McMaster Guideline Development Checklist Extension for Integrating Artificial Intelligence in the Guideline Enterprise (Guidelines‐Artificial Intelligence Extension)
Bibliographic record
Abstract
ABSTRACT Introduction Artificial intelligence (AI) has the potential to support processes of the guideline enterprise. To support the adequate preplanning and the transparent reporting of the use of AI in the guideline enterprise, the Guidelines International Network (GIN) proposed the development of an extension of the GIN–McMaster Guideline Development Checklist (GDC) for the use of AI in guidelines. Here we describe the protocol for the development of this extension. Methods We will follow a multiphase approach to generate an extension of the GIN–McMaster GDC. We will start by prompting a large language model to suggest relevant items for the extension. We will subsequently revise the obtained outputs in an iterative process involving the different elements of the working group. This iterative process will be informed by a scoping review. We will then reach different stakeholders to test the proposed checklist extension on their easiness of use, implementation, feasibility and reasonability. Questions This protocol lays the ground for the development of an extension to the GIN–McMaster GDC for the use of AI in guidelines. This extension will support the adequate use of AI in the guideline enterprise.
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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 | 0.194 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.136 | 0.032 |
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".