Prioritization approaches in the development of health practice guidelines: a systematic review
Bibliographic record
Abstract
BACKGROUND: Given the considerable efforts and resources required to develop practice guidelines, developers need to prioritize what topics and questions to address. This study aims to identify and describe prioritization approaches in the development of clinical, public health, or health systems guidelines. METHODS: We searched Medline and CINAHL electronic databases in addition to Google Scholar. We included papers describing prioritization approaches in sufficient detail allowing for reproducibility. We synthesized findings in a semi-quantitative way. We followed an iterative process to develop a common framework of prioritization criteria that captures all of the criteria reported by each included study. RESULTS: Our search captured 33,339 unique citations out of which we identified 10 papers reporting prioritization approaches for guideline development. All of the identified approaches focused on prioritizing guideline topics but none on prioritizing recommendation questions or outcomes. The two most frequently reported steps of the development process for these approaches were reviewing the grey literature (9 out of 10, 90%) and engaging various stakeholders (9 out of 10, 90%). We derived a common framework of 20 prioritization criteria that can be used when prioritizing guideline topics. The most frequently reported criteria were the health burden of disease which was included in all of the approaches, practice variation (8 out of 10, 80%), and impact on health outcomes (7 out of 10, 70%). Two of the identified approaches stood out as being comprehensive and detailed. CONCLUSIONS: We described 10 prioritization approaches in the development of health practice guidelines. There is a need to assess the effectiveness, efficiency and transparency of the identified approaches and to develop standardized and validated priority setting tools.
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 | 0.186 | 0.427 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.046 | 0.040 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".