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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

2017· review· en· W2774868550 on OpenAlexaff
Franco Grimaldi, Nicola Fazio, Roberto Attanasio, Andrea Frasoldati, Enrico Papini, Nadia Cremonini, Mariavittoria Davì, Luigi Funicelli, Sara Massironi, Francesca Spada, Vincenzo Toscano, Annibale Versari, Michele Zini, Massimo Falconi, Kjell Öberg

Bibliographic record

VenueEndocrine Metabolic & Immune Disorders - Drug Targets · 2017
Typereview
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMedicineGrading (engineering)Intensive care medicineDiseaseMultidisciplinary approachNeuroendocrine tumorsRisk stratificationOncologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.501
Teacher spread0.414 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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