NET Models Meeting 2024 white paper: the current state of neuroendocrine tumour research models and our future aspirations
Bibliographic record
Abstract
(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 two-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 the UK, EU, Israel, USA and Canada). Plenary talks were given by Prof. Thakker, who summarised NET research over the past 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 and 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 and 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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".