Consensus report of the 2021 National Cancer Institute neuroendocrine tumor clinical trials planning meeting
Bibliographic record
Abstract
Important progress has been made over the last decade in the classification, imaging, and treatment of neuroendocrine neoplasm (NENs), with several new agents approved for use. Although the treatment options available for patients with well-differentiated neuroendocrine tumors (NETs) have greatly expanded, the rapidly changing landscape has presented several unanswered questions about how best to optimize, sequence, and individualize therapy. Perhaps the most important development over the last decade has been the approval of 177Lu-DOTATATE for treatment of gastroenteropancreatic-NETs, raising questions around optimal sequencing of peptide receptor radionuclide therapy (PRRT) relative to other therapeutic options, the role of re-treatment with PRRT, and whether PRRT can be further optimized through use of dosimetry among other approaches. The NET Task Force of the National Cancer Institute GI Steering Committee convened a clinical trial planning meeting in 2021 with multidisciplinary experts from academia, the federal government, industry, and patient advocates to develop NET clinical trials in the era of PRRT. Key clinical trial recommendations for development included 1) PRRT re-treatment, 2) PRRT and immunotherapy combinations, 3) PRRT and DNA damage repair inhibitor combinations, 4) treatment for liver-dominant disease, 5) treatment for PRRT-resistant disease, and 6) dosimetry-modified PRRT.
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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".