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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".