Development of follow up recommendations for completely resected gastroenteropancreatic neuroendocrine tumours (GEP-NETS): Practice Survey of Commonwealth Neuroendocrine Tumour Collaboration (CommNETS) in conjunction with North American Neuroendocrine Tumour Society (NANETS).
Bibliographic record
Abstract
224 Background: Published guidelines for follow up after R0 resection of GEP-NETS are complex and emphasize closer surveillance in the first 3 years, as per other GI malignancies. NETS have a different pattern and timescale of recurrence, hence require more practical and tailored follow up. Aim: To examine real world follow up practices compared to published guidelines for follow up of patients with GEP-NETs. Methods: A detailed electronic cross-sectional survey was developed and distributed to members of the CommNETS Collaboration (Australia, NZ, Canada) and NANETs. Questions addressed demographics, knowledge and use of current guidelines and follow up practices relating to various prognostic factors. Descriptive statistics were obtained for all responses, stratified by country, patient volume and specialty. Results: There were 163 respondents: Australia: 59; NZ: 25; Canada: 46; US: 33. 50% were Medical Oncologists; 23% Surgeons; 13% Nuclear Medicine; 14% other. 38% of respondents were “very familiar” with NCCN NET guidelines; 33% with ENETS, 17% with ESMO, however only 15%, 27% and 10% found them “very useful”, respectively. 63% reported not using guidelines at their institution. Ranking of prognostic factors (top 5, decreasing order): grade, Ki67/mitotic count, T stage, N stage and site of origin. Follow up in 1st 2 years was most commonly every 6 months (62%); in years 3-5 every 12 months (59%) and > 5 years, 12 months (41%). Follow up patterns did not differ significantly by patient volume. The commonest investigations were CT scans (66%) and CgA (86%). When poor prognostic factors were introduced, increased visits and tests were recommended. Conclusions: This large international survey yields detailed information, highlighting variation in current follow up practices not well addressed by current guidelines. This forms a strong basis for the upcoming CommNETS/NANETS consensus meeting, which aims to produce user friendly, practical guidelines tailored to the expected pattern of recurrence in patients with 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".