Survey of challenges in access to diagnostics and treatment for neuroendocrine tumor patients (SCAN): The diagnostic process of GEP-NETs in Australia, Canada, China, France, Germany, the United Kingdom, and the United States of America.
Bibliographic record
Abstract
502 Background: SCAN assessed the global delivery of NET diagnostics and treatment. This analysis focused on the diagnostic process in gastroenteropancreatic (GEP) neuroendocrine tumor (NET) patients in countries with more robust respondent samples (at least 100 units of analysis): Australia (AU), Canada (CA), China (CH), France (FR), Germany (DE), United Kingdom (UK), and the United States (USA). Methods: During Sept-Nov 2019, 2359 NET patients & caregivers, and 436 healthcare professionals (HCPs) completed a self-reported online survey, available in 14 languages, disseminated via INCA and its partner networks. Results: 71% (1670/2359) were GEP-NET patients, 71% of which were from 7 countries (1188/1670), namely AU (7%, 120/1670), CA (9%, 154/1670), CH (7%, 114/1670), FR (8%, 137/1670), DE (9%, 149/1670), UK (11%, 191/1670) and USA (19%, 323/1670). Primary GEP-NETs were predominantly small intestinal (SI) with similar proportions in AU, CA, DE, FR,, UK and US and smaller in CH (*p < 0.001, Chi-squared). Second most common primary was pancreatic NET (similar across countries). Misdiagnosis was very frequent and occurred at least once but most commonly multiple times (table). The most frequent misdiagnoses were irritable bowel syndrome (AU 60%, CA 34%, CH 14%, FR 27%, DE 31%, UK 55%, USA 55%) and gastritis (AU 42%, CA 37%, CH 51%, FR 51%, DE 37%, UK 30%, USA 51%). Patients presented with stage IV disease in more than half of cases in 5 countries (table). On average three HCPs were involved in the diagnostic process in all above-mentioned countries. The HCPs who most often suggested the test that led to the correct diagnosis were gastroenterologists in CH 28%, FR 43%, DE 34%, USA 28%, and GPs in AU 28%, CA 27%, and UK 24% (45/191). In the majority of cases the diagnosis was received in a hospital without a NET specialist, except for CH (AU 38%; CA 42%; CH 25%; FR 36%, DE 51%, UK 44%, USA 45%). Conclusions: SCAN demonstrates some interesting geographical variations with respect to tumor demographics and stage at presentation. Nonetheless, delayed GEP-NET diagnosis remains a significant global challenge. Enhanced knowledge about GEP-NETs in hospitals without NET specialists, especially among gastroenterologists and family doctors (GPs), will drive improvements in global NET care.[Table: see text]
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".