The Panel Subgroup (PSG) Method: A Valuable Method for Effectively Engaging Panels in the Development of Practice Guidelines
Bibliographic record
Abstract
ABSTRACT Background Clinical practice guidelines rely on expert panels to review evidence and develop recommendations. The traditional approach of engaging experts often relies on periodic full panel meetings which can lead to slow progress and variability in panel engagement. To address these issues, the ASCO introduced the Panel Subgroup (PSG) method, dividing panels into smaller working groups to enhance engagement, distribute workload, and accelerate guideline development. Objective To evaluate the PSG method on guideline development timelines, its uptake, perceived benefits, challenges, and lessons learned from the perspective of ASCO guideline methodologists. Methods This study had two phases. Phase 1 was a retrospective review of ASCO guideline administrative data (2017–2024) comparing guidelines developed using the traditional or PSG method. Guidelines were included if they were de novo, had over 50 included studies, and complete administrative records. Data extracted included number of panelists, research questions, studies reviewed, subgroups (for PSG), and development time. Phase 2 involved qualitative interviews with methodologists who used the PSG method to explore experiences, benefits, and challenges. Results Sixteen guidelines met the inclusion criteria: seven used the traditional method and nine used the PSG method. The average development time was 13 months (SD, 2.41) for the PSG method compared to traditional (23 months; SD, 6.30). PSG uptake among methodologists was 75% (6/8). Reported benefits included improved expert engagement, deeper evidence analysis, better collaboration, and shared authorship. Challenges included greater time demands, increased coordination, writing inconsistencies, and the need for full panel buy‐in. Conclusion The PSG method offers a promising structure to improve engagement and streamline productivity. While not without challenges, it may be particularly useful for large panels and complex guidelines when applied thoughtfully.
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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 | 0.149 | 0.378 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads 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".