Moving forward through consensus: protocol for a modified Delphi approach to determine the top research priorities in the field of orthopaedic oncology
Bibliographic record
Abstract
INTRODUCTION: Orthopaedic oncology researchers face several obstacles in the design and execution of randomised controlled trials, including finite fiscal resources to support the rising costs of clinical research and insufficient patient volume at individual sites. As a result, high-quality research to guide clinical practice has lagged behind other surgical subspecialties. A focused approach is imperative to design a research programme that is economical, streamlined and addresses clinically relevant endpoints. The primary objective of this study will be to use a consensus-based approach to identify research priorities for international clinical trials in orthopaedic oncology. METHODS AND ANALYSIS: We will conduct a 3-phase modified Delphi method consisting of 2 sequential rounds of anonymous web-based questionnaires (phases I and II), and an in-person consensus meeting (phase III). Participants will suggest research questions that they believe are of particular importance to the field (phase I), and individually rate each proposed question on 5 criteria (phase II). Research questions that meet predetermined consensus thresholds will be brought forward to the consensus meeting (phase III) for discussion by an expert panel. Following these discussions, the expert panel will be asked to assign scores for each research question, and research questions meeting predetermined criteria will be brought forward for final ranking. The expert panel will then be asked to rank the top 3 research questions, and these 3 research questions will be distributed to the initial group of participants for validation. ETHICS AND DISSEMINATION: An ethics application is currently under review with the Hamilton Integrated Research Ethics Board in Hamilton, Ontario, Canada. The results of this initiative will be disseminated through peer-reviewed publications and conference presentations.
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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 | 0.204 | 0.211 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.051 | 0.019 |
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".