F-140USE OF THE DELPHI PROCESS TO ACHIEVE CONSENSUS IN DEVELOPING A RANDOMIZED CONTROLLED TRIAL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it