How to write a guideline: a proposal for a manuscript template that supports the creation of trustworthy guidelines
Bibliographic record
Abstract
Trustworthy health guidelines should provide recommendations, document the development process, and highlight implementation information. Our objective was to develop a guideline manuscript template to help authors write a complete and useful report. The McMaster Grading of Recommendations Assessment, Development and Evaluation Centre collaborated with the American Society of Hematology (ASH) to develop guidelines for the management of venous thromboembolism. A template for reporting the guidelines was developed based on prior approaches and refined using input from other key stakeholders. The proposed guideline manuscript template includes: (1) title for guideline identification, (2) abstract, including a summary of key recommendations, (3) overview of all recommendations (executive summary), and (4) the main text, providing sufficient detail about the entire process, including objectives, background, and methodological decisions from panel selection and conflict-of-interest management to criteria for updating, as well as supporting information, such as links to online (interactive) tables. The template further allows for tailoring to the specific topic, using examples. Initial experience with the ASH guideline manuscript template was positive, and challenges included drafting descriptions of recommendations involving multiple management pathways, tailoring the template for a specific guideline, and choosing key recommendations to highlight. Feedback from a larger group of guideline authors and users will be needed to evaluate its usefulness and refine. The proposed guideline manuscript template is the first detailed template for transparent and complete reporting of guidelines. Consistent application of the template may simplify the preparation of an evidence-based guideline manuscript and facilitate its use.
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 | 0.268 | 0.625 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.030 | 0.043 |
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".