Past, Present, and Future of Molecular and Cellular Oncology
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the last 20 years, the field of cellular and molecular oncology has been born and has moved its first steps, with an increasingly rapid pace. Hundreds of oncogenic and oncosuppressive signaling cascades have been characterized, facilitating the development of an ever more refined and variegated arsenal of diagnostic and therapeutic weapons. Furthermore, several cancer-specific features and processes have been identified that constitute promising therapeutic targets. For instance, it has been demonstrated that microRNAs can play a critical role in oncogenesis and tumor suppression. Moreover, it turned out that tumor cells frequently exhibit an extensive metabolic rewiring, can behave in a stem cell-like fashion (and hence sustain tumor growth), often constitutively activate stress response pathways that allow them to survive, can react to therapy by engaging in non-apoptotic cell death programs, and sometimes die while eliciting a tumor-specific immune response. In this Perspective article, we discuss the main issues generated by these discoveries that will be in the limelight of molecular and cellular oncology research for the next, hopefully few years.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it