Italian Association of Clinical Endocrinologists (AME) and Italian AACE Chapter Position Statement for Clinical Practice: Assessment of Response to Treatment and Follow-Up in Gastroenteropancreatic Neuroendocrine Neoplasms
Bibliographic record
Abstract
Well-established criteria for evaluating the response to treatment and the appropriate followup of individual patients are critical in clinical oncology. The current evidence-based data on these issues in terms of the management of gastroenteropancreatic (GEP) neuroendocrine neoplasms (NEN) are unfortunately limited. This document by the Italian Association of Clinical Endocrinologists (AME) on the criteria for the follow-up of GEP-NEN patients is aimed at providing comprehensive recommendations for everyday clinical practice based on both the best available evidence and the combined opinion of an interdisciplinary panel of experts. The initial risk stratification of patients with NENs should be performed according to the grading, staging and functional status of the neoplasm and the presence of an inherited syndrome. The evaluation of response to the initial treatment, and to the subsequent therapies for disease progression or recurrence, should be based on a cost-effective, risk-effective and timely use of the appropriate diagnostic resources. A multidisciplinary evaluation of the response to the treatment is strongly recommended and, at every step in the follow-up, it is mandatory to assess the disease state and the patient performance status, comorbidities, and recent clinical evolution. Local expertise, available technical resources and the patient preferences should always be evaluated while planning the individual clinical management of GEP-NENs.
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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 | 0.030 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".