Quality of Surveillance in Patients with Completely Resected Gastroenteropancreatic Neuroendocrine Tumours
Bibliographic record
Abstract
The increasing incidence of gastroenteropancreatic neuroendocrine tumours (GEP-NETs) underscores the need for effective post-surgery surveillance. We evaluated adherence to the 2018 follow-up recommendations from the Commonwealth Neuroendocrine Tumour Collaboration (CommNETs) for surveillance practices among patients with completely resected GEP-NETs at the University Health Network. Our hypothesis posited a misalignment between our surveillance practices and both the natural disease history and guideline recommendations. Findings revealed a low adherence rate of 23%, with many patients undergoing unnecessary investigations or becoming lost to follow-up, indicating both a potential over- and underutilization of surveillance practices. Physician influence significantly affected adherence, with moderately differentiated tumours and patients followed by medical oncology showing higher adherence rates. Overuse of investigations could lead to resource strain, heightened patient anxiety, and increased radiation exposure risks. Our study emphasizes the critical need for guideline adherence, protocol standardization, and resource optimization in GEP-NET surveillance to enhance patient outcomes, including early recurrence detection and improved survival rates.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".