Survey of challenges in access to diagnostics and treatment for neuroendocrine tumor (NET) patients (SCAN): Awareness of specialized techniques and latest interventions.
Bibliographic record
Abstract
e16708 Background: SCAN measured global readiness to provide diagnostics and treatments for NET patients in terms of awareness, availability, quality and affordability. This analysis focused on patient and healthcare professional (HCP) awareness of NET diagnostics and treatments. Methods: During Sept-Nov 2019, NET patients and HCPs completed an online survey (available in 14 languages). Results: There were 2795 respondents from 68 countries across 6 continents (2359 patients/carers; 436 HCPs). Primary NETs were most often gastroenteropancreatic NETs (GEP NET; 71% [1408/1983]), particularly small intestinal (35% [690/1983]) or pancreatic (20% [402/1983]). Biopsy was the most well-known diagnostic option in the overall NET patient group (82% [1917/2325]), the GEP NET patient subgroup (83% [1156/1395]) and HCPs (94% [411/435]), followed by CT (all patients: 81% [1874/2325]; GEP NET: 80% [1118/1395]; HCPs: 86% [376/435]). More HCPs were aware of specialized diagnostics, such as 68Ga-DOTA PET CT (HCP 81% [353/435]) and chromogranin A (CgA; 79% [344/435]), than patients (all: 68% [1574/2325] & 62% [1451/2325], respectively; GEP NET: 69% [962/1395] & 67% [936/1395]). The vast majority of all patients (87% [1983/2275]), GEP NET patients (89% [1215/1363]) and HCPs (91% [392/431]) knew surgery was a treatment option. Somastatin analogues were recognised as a treatment option by 90% of HCPs (387/431), but only 75% of GEP NET patients (1019/1363) and 70% of all NET patients (1599/2275). Nearly a quarter of HCPs (22% [95/431]) and one-third of patients (all: 33% [755/2275]; GEP NET: 30% [409/1363]) had not heard of peptide receptor radionuclide therapy (PRRT). The majority of patients (all: 88% [2007/2273]; GEP NET: 89% [1213/1370]) and HCPs (93% [396/425]) were aware of conventional imaging, such as CT/MRI/ultrasound, being used for ongoing monitoring of NETs. Approximately a third of all NET patients and a quarter of HCPs were unware CgA (patients: 32% [723/2273]; HCPs: 22% [94/425]) or 68Ga-DOTA PET CT (patients: 29% [670/2273]; HCPs: 24% [102/425]) were ongoing monitoring tools. Similarly, CgA and 68Ga-DOTA PET CT were not recognized by 27% of GEP NET patients (364/1370 & 371/1370, respectively). Conclusions: Increased awareness of NET diagnostics and treatments, particularly newer, more specialized tools, amongst both HCPs and patients is required to ensure continued advancements and improvements in the global standard of care for 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".