Development of a prioritisation tool for the updating of clinical guideline questions: the UpPriority Tool protocol
Bibliographic record
Abstract
INTRODUCTION: Due to a continuous emergence of new evidence, clinical guidelines (CGs) require regular surveillance of evidence to maintain their trustworthiness. The updating of CGs is resource intensive and time consuming; therefore, updating may include a prioritisation process to efficiently ensure recommendations remain up to date. The objective of our project is to develop a pragmatic tool to prioritise clinical questions for updating within a CG. METHODS AND ANALYSIS: To develop the tool, we will use the results and conclusions of a systematic review of methodological research on prioritisation processes for updating and will adopt a methodological approach we have successfully implemented in a previous experience.We will perform a multistep process including (1) generation of an initial version of the tool, (2) optimisation of the tool (feasibility test of the tool, semistructured interviews, Delphi consensus survey, external review by CG methodologists and users and pilot test of the tool) and (3) approval of the final version of the tool.At each step of the process, we will (1) calculate absolute frequencies and proportions (quantitative data), (2) use content analysis to summarise and draw conclusions (qualitative data) and (3) draft a final report, discuss results and refine the previous versions of the tool. Finally, we will calculate intraclass coefficients with 95% CIs for each item and overall as indicators of agreement among reviewers. ETHICS AND DISSEMINATION: We have obtained a waiver of approval from the Clinical Research Ethics Committee at the Hospital de la Santa Creu i Sant Pau (Barcelona). The results of the study will be published in peer-reviewed journal and communicated to interested stakeholders.The tool could support the standardisation of prioritisation processes for updating CGs and therefore have important implications for a more efficient use of resources in the CG field.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".