P329 Developing A Strategy To Assess The Reporting Of The Updating Process In Clinical Practice Guideline: A Draft Checklist
Bibliographic record
Abstract
Background Scientific knowledge is in constant change and, therefore, clinical practice guidelines (CPGs) require a frequent reassessment. However, the best methodology for updating CPGs is not known and methods are poorly reported in CPGs. A framework to evaluate the quality of reporting the updating process in CPGs and to provide guidance for minimum thresholds for an updating strategy is needed. Objective To develop a CPG update reporting checklist. Methods An initial list of items has been developed based in a systematic review done for our team about the guidance in updating handbooks. The working group has reviewed the initial list and reached consensus about the items to include. Through a survey, we will present the initial list to a multidisciplinary group of international experts and we will ask them about what is the relevant information that needs to be reported in an updated CPG, and about the elements to be included in a high-quality updating process. Results These results will give us insight in what elements are required to be reported an updated CPG. Additionally, we will gain information about what kind of elements include a high-quality updating process. Discussion and Implications This checklist will help people responsible for updating CPGs, in conducting and reporting their update in a high-quality manner. Ultimately this might results in more up-to-date recommendations and more valid CPGs.
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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 | 0.371 | 0.607 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".