Supplementary Material for: Follow-Up for Resected Gastroenteropancreatic Neuroendocrine Tumours: A Practice Survey of the Commonwealth Neuroendocrine Tumour Collaboration (CommNETS) and the North American Neuroendocrine Tumor Society (NANETS)
Bibliographic record
Abstract
Objectives: There is no consensus regarding optimal follow-up in resected gastroenteropancreatic neuroendocrine tumours (NETs). We aimed to perform a practice survey to ascertain follow-up patterns by health care practitioners and highlight areas of variation that may benefit from further quantitative research. Methods: A Web-based survey targeted at NET health care providers in Australia, New Zealand, Canada, and the USA was developed by a steering committee of medical oncologists and a research methodologist. Thirty-seven questions elicited information regarding adherence to guidelines, the influence of risk factors on follow-up, and the frequency and choice of modality in follow-up. Results: There were 163 respondents: 59 from Australia, 25 from New Zealand, 46 from Canada, and 33 from the USA (50% medical oncology, 23% surgery, 13% nuclear medicine, and 15% other). Thirty-eight percent of the respondents were “very familiar” with the NCCN NET guidelines, 33% with the ENETS guidelines, and 17% with the ESMO guidelines; however, only 15, 27, and 10%, respectively, found them “very useful”; 63% reported not using guidelines at their institution. The commonest investigations used were CT scans (66%) and chromogranin A (86%). The US respondents were more likely to follow patients up past 5 years, and the Australian respondents utilized more functional and less cross-sectional imaging. When poor prognostic factors were introduced, the respondents recommended more visits and tests. Conclusions: This large international survey highlights variation in current follow-up practices not well addressed by the current guidelines. More quantitative research is required to inform the development of evidence-based guidelines tailored to the pattern of recurrence in NETs.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.635 | 0.108 |
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".