Vitality, viability, long-term clonogenic survival, cytotoxicity, cytostasis and lethality: what do they mean when testing new investigational oncology drugs?
Bibliographic record
Abstract
In the field of experimental therapeutics for oncology purposes researchers are continuously evaluating the toxicity of novel treatment approaches against cancer cells. Within this topic of research, it is highly critical to define parameters of toxicity that denote when cancer cells are perturbed in their functionality by a new investigational drug. As the goal for these approaches is to achieve cellular demise, then what approaches to use and what do they mean in terms of assessing such cell death is of critical importance. In this comment article we highlight the definition of vitality and differentiate it from viability, and further define clonogenic survival in a chronic fashion. Additionally, we highly recommend the use of the term cytotoxicity as a general descriptor indicating toxicity towards a cell, but within that we encourage to sub-classify it as either cytostasis (i.e., when a treatment does not allow a cell to grow but it does not kill it either), or lethality (when a cell dies in response to the treatment). A more precise use of these terms should help advance the field of experimental therapeutics in oncology towards better defining the mechanisms of action of novel investigational drugs.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.037 | 0.041 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".