Evaluation and adaptation of clinical practice guidelines
Bibliographic record
Abstract
Clinical practice guidelines are “systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances.”1 They are intended to offer concise instructions on how to provide healthcare services.2 The most important benefit of clinical practice guidelines is their potential to improve both the quality or process of care and patient outcomes.3 Increasingly, clinicians and clinical managers must choose from numerous, sometimes differing, and occasionally contradictory, guidelines.4 This situation is further complicated by concerns about the quality of available guidelines.5,6,7,8,9,10,11 Indeed, adoption of guidelines of questionable validity can lead to the use of ineffective interventions, inefficient use of scarce resources, and perhaps most importantly, harm to patients.12,13 Determining which guidelines are quality products worthy of adoption can be daunting. Every effort should be made to identify existing guidelines that have been rigorously developed and to adopt or adapt them for local use.12 However, organisations and clinicians should scrutinise the methods by which the guidelines were developed, as well as the content and utility of the recommendations. Even guidelines developed by prominent professional groups or government bodies should not be exempt from this scrutiny as it has been shown that these guidelines may be of substandard quality.10 The Practice Guidelines Evaluation and Adaptation Cycle14,15 is a framework for organising and making decisions about which high quality guidelines to adopt (figure). Although the cycle was originally intended for use by organisations and groups wanting to implement best practice, most steps of the process are also helpful in guiding evaluation of guidelines by individual clinicians. This Users’ guide will describe strategies for identifying, critically appraising, and adopting or adapting guidelines for local 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 | 0.446 | 0.730 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".