Consideration of quality of life in the treatment decision-making for patients with advanced gastroenteropancreatic neuroendocrine tumors
Bibliographic record
Abstract
INTRODUCTION: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are a complex and heterogenous family of solid malignancies that originate from neuroendocrine tissue in the gastrointestinal tract or pancreas. Most patients diagnosed with GEP-NETs present with advanced or metastatic disease, and quality of life (QoL) is often an important priority when selecting treatments for these patients. Patients with advanced GEP-NETs often experience a substantial and persistent symptom burden that undermines their QoL. Addressing a patient's individual symptoms through judicious selection of treatment may improve QoL. AREAS COVERED: The objectives of this narrative review are to summarize the impact of advanced GEP-NETs on patient QoL, assess the potential value of current treatments for maintaining or improving patient QoL, and offer a clinical framework for how these QoL data can be translated to inform clinical decision-making for patients with advanced GEP-NETs. EXPERT OPINION: Patients with advanced GEP-NETs experience a significant and persistent symptom burden that impacts their daily lifestyle, activities, work life, and financial health, leading to erosion of their QoL. Ongoing and future studies incorporating longitudinal QoL assessments and head-to-head treatment evaluations will further inform the incorporation of QoL into clinical decision-making.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".