The development of the GIN‐McMaster checklist extension for guideline adaptation protocol
Bibliographic record
Abstract
Abstract Background To ensure rigour and transparency in guideline adaptation and contextualization processes, standardized tools and methodological principles are needed. However, methodological challenges have been continuously documented in guideline adaptation processes. Objective To develop a guideline international network (GIN)‐McMaster guidelines development checklist (GDC) extension for guideline adaptation. Methods This project follows multiphase iterative approach, including (1) compiling a list of key methodological steps for guideline adaptation, based on scoping reviews of the current knowledge of guideline adaptation; (2) proposing methodological principles for guideline adaptation based on key methodological steps, in parallel to developing the GIN‐McMaster Guideline Development Checklist extension for adaptation (GDC‐adaptation extension) concerning the original checklist and methodological steps for adaptation; (3) iteratively refining the methodological steps through GIN Adaptation working group discussions, and GDC‐adaptation extension items through several rounds of Delphi consensus survey and (4) Public consultation and finalization. For methodological principles, this involves public consultation; for GDC‐adaptation extension, we will conduct user testing through semi‐structured interviews. Finally, we will submit the final outputs to the GIN board and seek final approval. Discussion The identification of the key methodological principles, together with the GIN‐McMaster GDC extension for guideline adaptation and contextualization, will provide clarity in the planning and execution of adaptation, adoption and/or development of recommendations. The aim of the checklist is to improve efficiency and reduce research waste in guideline development while maintaining rigour and transparency.
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 | 0.227 | 0.395 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.054 | 0.019 |
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".