NET models meeting 2024: the current state of neuroendocrine tumour research models and our future aspirations
Bibliographic record
Abstract
Current models for the study of neuroendocrine tumours (NETs) are severely limited. While in vitro (e.g. cell lines), ex vivo (e.g. organoids), and in vivo (e.g. mice) models all exist, each has limitations. To address these limitations and collectively identify strategies to move the NET models field forward, we held an inaugural NET Models meeting, hosted by our founding group: Dr. Lines (Oxford); Prof. Quelle (Iowa); Dr. Dayton (Barcelona); Dr. Ear (Iowa); Dr. Marinoni (Bern); and Dr. Guenter (Alabama). This 2-day meeting in Oxford (UK) was organised and supported by Bioscientifica Ltd and was solely dedicated to the discussion of NET models. The meeting was attended by 30 international researchers (from UK, EU, Israel, USA and Canada). Plenary talks were given by Prof. Thakker who summarised NET research over the last few decades, and Dr. Schrader who described the process and pitfalls of generating new cell lines. Eight researchers also presented their work on topics ranging from human cell 3D bioprinting, to zebrafish models, and included novel ideas as well as improvements on current concepts. This was followed by an interactive workshop where discussion topics included, a summary of currently available NET models, limitations of these models, barriers to developing new models, and how we can address these issues going forward. This white paper summarises the key points raised in these discussions, as well as the future aspirations of the NET Models Consortium. The next meeting will take place in Oxford (UK) in 2025, contact contact@netcancerfoundation.com for more information.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.022 |
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".