Patient-Reported Burden of a Neuroendocrine Tumor (NET) Diagnosis: Results From the First Global Survey of Patients With NETs
Bibliographic record
Abstract
PURPOSE: Despite the considerable impact of neuroendocrine tumors (NETs) on patients' daily lives, the journey of the patient with a NET has rarely been documented, with published data to date being limited to small qualitative studies. NETs are heterogeneous malignancies with nonspecific symptomology, leading to extensive health care use and diagnostic delays that affect survival. A large, international patient survey was conducted to increase understanding of the experience of the patient with a NET and identify unmet needs, with the aim of improving disease awareness and care worldwide. METHODS: An anonymous, self-reported survey was conducted (online or on paper) from February to May 2014, recruiting patients with NETs from > 12 countries as a collaboration between the International Neuroendocrine Cancer Alliance and Novartis Pharmaceuticals. Survey questions captured information on sociodemographics, clinical characteristics, NET diagnostic experience, disease impact/management, interaction with medical teams, NET knowledge/awareness, and sources of information. This article reports the most relevant findings on patient experience with NETs and the impact of NETs on health care system resources. RESULTS: A total of 1,928 patients with NETs participated. A NET diagnosis had a substantially negative impact on patients' personal and work lives. Patients reported delayed diagnosis and extensive NET-related health care resource use. Patients desired improvement in many aspects of NET care, including availability of a wider range of NET-specific treatment options, better access to NET experts or specialist centers, and a more knowledgeable, better-coordinated/-aligned NET medical team. CONCLUSION: This global patient-reported survey demonstrates the considerable burden of NETs with regard to symptoms, work and daily life, and health care resource use, and highlights considerable unmet needs. Further intervention is required to improve the patient experience among those with NETs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".