Guideline adaptation: an approach to enhance efficiency in guideline development and improve utilisation
Bibliographic record
Abstract
BACKGROUND: Developing and updating high-quality guidelines requires substantial time and resources. To reduce duplication of effort and enhance efficiency, we developed a process for guideline adaptation and assessed initial perceptions of its feasibility and usefulness. METHODS: Based on preliminary developments and empirical studies, a series of meetings with guideline experts were organised to define a process for guideline adaptation (ADAPTE) and to develop a manual and a toolkit made available on a website (http://www.adapte.org). Potential users, guideline developers and implementers, were invited to register and to complete a questionnaire evaluating their perception about the proposed process. RESULTS: The ADAPTE process consists of three phases (set-up, adaptation, finalisation), 9 modules and 24 steps. The adaptation phase involves identifying specific clinical questions, searching for, retrieving and assessing available guidelines, and preparing the draft adapted guideline. Among 330 registered individuals (46 countries), 144 completed the questionnaire. A majority found the ADAPTE process clear (78%), comprehensive (69%) and feasible (60%), and the manual useful (79%). However, 21% found the ADAPTE process complex. 44% feared that they will not find appropriate and high-quality source guidelines. DISCUSSION: A comprehensive framework for guideline adaptation has been developed to meet the challenges of timely guideline development and implementation. The ADAPTE process generated important interest among guideline developers and implementers. The majority perceived the ADAPTE process to be feasible, useful and leading to improved methodological rigour and guideline quality. However, some de novo development might be needed if no high quality guideline exists for a given topic.
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 | 0.233 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".