The implementation of prioritization exercises in the development and update of health practice guidelines: A scoping review
Bibliographic record
Abstract
BACKGROUND: The development of trustworthy guidelines requires substantial investment of resources and time. This highlights the need to prioritize topics for guideline development and update. OBJECTIVE: To systematically identify and describe prioritization exercises that have been conducted for the purpose of the de novo development, update or adaptation of health practice guidelines. METHODS: We searched Medline and CINAHL electronic databases from inception to July 2019, supplemented by hand-searching Google Scholar and the reference lists of relevant studies. We included studies describing prioritization exercises that have been conducted during the de novo development, update or adaptation of guidelines addressing clinical, public health or health systems topics. Two reviewers worked independently and in duplicate to complete study selection and data extraction. We consolidated findings in a semi-quantitative and narrative way. RESULTS: Out of 33,339 identified citations, twelve studies met the eligibility criteria. All included studies focused on prioritizing topics; none on questions or outcomes. While three exercises focused on updating guidelines, nine were on de novo development. All included studies addressed clinical topics. We adopted a framework that categorizes prioritization into 11 steps clustered in three phases (pre-prioritization, prioritization and post-prioritization). Four studies covered more than half of the 11 prioritization steps across the three phases. The most frequently reported steps for generating initial list of topics were stakeholders' input (n = 8) and literature review (n = 7). The application of criteria to determine research priorities was used in eight studies. We used and updated a common framework of 22 prioritization criteria, clustered in 6 domains. The most frequently reported criteria related to the health burden of disease (n = 9) and potential impact of the intervention on health outcomes (n = 5). All the studies involved health care providers in the prioritization exercises. Only one study involved patients. There was a variation in the number and type of the prioritization exercises' outputs. CONCLUSIONS: This review included 12 prioritization exercises that addressed different aspects of priority setting for guideline development and update that can guide the work of researchers, funders, and other stakeholders seeking to prioritize guideline topics.
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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 | 0.390 | 0.602 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.055 | 0.052 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".