Protocol of an interdisciplinary consensus project aiming to develop an AGREE II extension for guidelines in surgery
Bibliographic record
Abstract
INTRODUCTION: Appraisal of Guidelines for Research and Evaluation (AGREE II) is an instrument that informs development, reporting and assessment of clinical practice guidelines. Previous research has demonstrated the need for improvement in methodological and reporting quality of clinical practice guidelines specifically in surgery. We aimed to develop an AGREE II extension document for application in surgical guidelines. METHODS AND ANALYSIS: We have performed a structured literature review and assessment of guidelines in surgery using the AGREE II instrument. In exploratory analyses, we have identified factors associated with guideline quality. We have performed reliability and factor analyses to inform the development of an extension document. We will summarise this information and present it to a Delphi panel of stakeholders. We will perform iterative Delphi rounds and we will summarise the final results to develop the extension instrument in a dedicated consensus conference. ETHICS AND DISSEMINATION: Funding bodies will not be involved in the development of the instrument. Research ethics committee and Health Research Authority approval was waived, since this is a professional staff study only and no duty of care lies with the National Health Service to any of the participants. Conflicts of interest, if any, will be addressed by reassigning functions or replacing participants with relevant conflicts. The results will be disseminated through publication in peer reviewed journals, the funders' websites, social media and direct contact with guideline development organisations and peer-reviewed journals that publish guidelines.
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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 | 0.264 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.044 | 0.013 |
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".