F-140USE OF THE DELPHI PROCESS TO ACHIEVE CONSENSUS IN DEVELOPING A RANDOMIZED CONTROLLED TRIAL
Bibliographic record
Abstract
Objectives: Currently, there is no evidence to show that intensive follow-up after resection of non-small cell lung cancer (NSCLC) improves survival. An attempt to develop a protocol for a randomized trial of standard follow-up for resected NSCLC failed to achieve consensus on the two arms of the proposed trial at a face-to-face meeting of Canadian Thoracic Surgeons. The purpose of this study was to use a Delphi method to establish the standard arm for the study. Methods: All thoracic surgeons in Canada were asked to complete three electronic surveys. The first round (R1) involved questions about follow-up practices (i.e. time intervals, imaging). Round two (R2) collated the responses from R1, which were used to suggest standard and intensive follow-up protocols for R3. Round three (R3) presented the final protocols to determine willingness of surgeons to enroll patients in the RCT using these study arms. Results: Fourty-eight participants (64% of Canadian Thoracic Surgeons) responded in R1. All 48 followed patients after NSCLC resection, and felt establishing a protocol through an RCT was worthwhile. A standard protocol was used by 40 surgeons (83%) and 30 (62.5%) used the same protocol for all stages. Most respondents used CT in follow-up, and only 1 used MRI/CT brain or PET scan. No respondents used bone scans. Respondents felt it was important to detect asymptomatic locoregional recurrence (44; 91.7%) and metastatic disease (30; 62.5%). Only 29 participants (37%) responded in R2 and R3. Using feedback from R2, the final protocols were presented in R3. Conclusions: The modified Delphi method was successfully used to develop standard and intensive follow-up arms for a RCT to develop a follow-up protocol for NSCLC. Disclosure: No significant relationships.
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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 | 0.623 | 0.683 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".