Identifying and Prioritizing Gaps in Neuroendocrine Tumor Research: A Modified Delphi Process With Patients and Health Care Providers to Set the Research Action Plan for the Newly Formed Commonwealth Neuroendocrine Tumor Collaboration
Bibliographic record
Abstract
PURPOSE: Neuroendocrine tumors (NETs) are a diverse group of malignancies that pose challenges common to all rare tumors. The Commonwealth Neuroendocrine Tumor Collaboration (CommNETS) was established in 2015 to enhance outcomes for patients with NETs in Canada, Australia, and New Zealand. A modified Delphi process was undertaken involving patients, clinicians, and researchers to identify gaps in NETs research to produce a comprehensive and defensible research action plan. METHODS: A three-round modified Delphi process was undertaken with larger representation than usual for medical consensus processes. Patient/advocate and health care provider/researcher expert panels undertook Round 1, which canvassed 17 research priorities and 42 potential topics; in Round 2, these priorities were ranked. Round 3 comprised a face-to-face meeting to generate final consensus rankings and formulate the research action plan. RESULTS: The Delphi groups consisted of 203 participants in Round 1 (64% health care providers/researchers, 36% patient/advocates; 52% Canadian, 32% Australian, and 17% New Zealander), of whom 132 participated in Round 2. The top eight priorities were biomarker development; peptide receptor radionuclide therapy optimization; trials of new agents in advanced NETs; functional imaging; sequencing therapies for metastatic NETs, including development of validated surrogate end points for studies; pathologic classification; early diagnosis; interventional therapeutics; and curative surgery. Two major areas were ranked significantly higher by patients/advocates: early diagnosis and curative surgery. Six CommNETS working parties were established. CONCLUSION: This modified Delphi process resulted in a well-founded set of research priorities for the newly formed CommNETS collaboration by involving a large, diverse group of stakeholders. This approach to setting a research agenda for a new collaborative group should be adopted to ensure that research plans reflect unmet needs and priorities in the field.
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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 | 0.244 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".