Treatments and ongoing monitoring for patients with neuroendocrine tumors, monitored by medical oncologists: Scan comparative data between advanced economies (AE) and emerging and developing economies (EDE).
Bibliographic record
Abstract
e16200 Background: The Survey of Challenges in Access to Diagnostics and Treatment for NET Patients (SCAN) measured the delivery of healthcare to neuroendocrine tumor (NET) patients globally. This analysis focused on the treatments and follow-up received by NET patients who most often visited a medical oncologist (MO) for their ongoing monitoring, and compared results between Advanced Economies (AE) and Emerging and Developing Economies (EDE). Methods: During Sept-Nov 2019, 2359 NET patients and 436 healthcare professionals (HCPs) from 68 countries completed an online self-report survey, available in 14 languages, disseminated by NET patient group networks, NET medical societies and other INCA partners. Results: 1016 NET patients (43% of all NET patients globally) reported a MO as the HCP most often visited for their ongoing monitoring, 90% of which were from AE (N = 913) and 10% from EDE (N = 103). 108 MOs (25% of all HCPs) took part in the survey, 62% from EDE [67/108]. Primary NETs for this patient sub-group were most often GEP NETs, specifically small intestine, more often reported from AE (41%, 316/913) than EDE (20%, 21/103; p < 0.0001 by Chi-square), and pancreatic, more often reported from EDE (33%, 34/103) than AE (21%, 192/913). Other primary NETs included lung (AE [11%, 92/913], EDE [6%, 6/103]) and of unknown origin (AE [8%, 73/913], EDE [14%, 14/103]). The most common treatment received was somatostatin analogues (SSA) (AE [57%, 507/913], EDE [44%, 43/103]), followed by surgery (AE [16%, 140/913], EDE [17%, 16/103]) and oral chemotherapy (AE [14%, 120/913], EDE [18%, 17/103]). PRRT (AE [14%, 124/913], EDE [8%, 8/103]; p < 0.0001) was used more frequently in AE. Awareness of the 4 most frequently used treatments among MOs was 80% or greater. MOs reported similar availability of SSA (AE [95%, 39/41], EDE [96%, 64/67]) by economic areas, while lower availability of surgery (AE [98%, 40/41], EDE [88%, 59/67], oral chemotherapy (AE [98%, 40/41], EDE [82%, 55/67]) and PRRT (AE [81%, 33/41], EDE [60%, 40/67]; p < 0.0001) in EDE. NET patients reported CT scan as the most frequently used ongoing monitoring tool (AE [78%, 699/913], EDE [70%, 66/103]). Ga-68-labeled SSA PET/CT was used for slightly more than 1/3 of patients with no significant differences by regions (AE [38%, 337/913], EDE [30%, 28/103]). For these tools, awareness among MOs was 69% and above, while both awareness and availability were significantly lower in EDE. Multidisciplinary teams (MDT) were rarely used in AE NET patients (35%, 318/913), and in only 14% (14/103) of EDE. Conclusions: MOs play an essential role in NET patients’ follow-up, being the leading HCP for almost half of them. There is a critical need for a global standard of ongoing NET monitoring as data indicate significant differences in therapeutic and follow-up procedures between AE and EDE.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".