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 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 | 0.000 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".