Abstract IA03: Head and Neck Cancer Takes a Nerve, Cancer neuroscience: emerging hallmark of cancer
Bibliographic record
Abstract
Abstract Over the past decade, emerging technologies have enabled investigators to begin to bridge the gap between cancer and neuroscience research to unveil the interactive roles of nerves in cancer. We are now beginning to define how the nervous system contributes to cancer initiation, growth, spread, recurrence, and even resistance to oncologic therapeutic strategies. Indeed, neuromodulation with both genetic and pharmacological approaches has been shown to affect not only tumor growth but also antitumor immune response. Collectively, studies have demonstrated a significant and pressing need to recognize cancer neuroscience interplay as a hallmark of cancer to 1) establish the neoneurogenic process as a highly relevant therapeutic target for both the prevention and treatment of cancer, 2) foster interdisciplinary cross-talk among experts in cancer biology and neuroscience, which have traditionally progressed along parallel paths, and 3) integrate human variables such as biological sex, age, race, and gender as underlying factors that can impact cancer neuroscience. Citation Format: Moran Amit. Head and Neck Cancer Takes a Nerve, Cancer neuroscience: emerging hallmark of cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr IA03.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".