Economics of gastroenteropancreatic neuroendocrine tumors: a systematic review
Bibliographic record
Abstract
Background: Despite current interest, enthusiasm and progress in the development of therapies for gastroenteropancreatic (GEP) neuroendocrine tumors (NETs), there are substantial gaps in the published literature regarding cost-of-illness analyses, economic evaluation and budget impact analyses. Compounding the issue is that data on resource utilization and cost-effectiveness of different diagnostic and therapeutic modalities for GEP-NETs are scarce. Methods: A systematic review on the economic impact of GEP-NETs was carried out using four databases: EMBASE, PubMed, the National Health Service Economic Evaluation Database and Cochrane review. Fully published articles from January 2000 to May 2017, in English and Spanish, were included. All articles that satisfied the inclusion criteria were included in the systematic review; summary descriptive statistics were used to describe the methodological characteristics. Results: The 14 studies selected included cost-of-illness analyses ( n = 4), economic evaluations ( n = 7) and budget impact analyses ( n = 3). Almost all studies were performed in the United States. Healthcare costs for patients with NETs included medication, outpatient visits, hospitalizations, and check-ups/tests. Reducing adverse events is an area where cost savings could be achieved; however, there was not enough evidence on the cost impact of adverse events. Conclusion: There is a lack of data related to resource utilization in the field of GEP-NETs. Therefore, cost-effectiveness and budget impact studies of existing and emerging treatments are urgently needed to help the decision-making process for patients with NETs.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".