Cell Death Identification in Anticancer Therapy—Letter
Bibliographic record
Abstract
In their recent review, Rello-Varona and colleagues (1) emphasize that “The correct evaluation of cell death in every experimental setting related to cancer research is a must.” Accordingly, they discuss the variety of methods to identify dead cells and to distinguish the various forms of cell death from one another. While we agree with their descriptions of the different forms of cell death and appreciate that the mechanism of cell death is often worth characterizing in cancer research, we would emphasize three aspects of cell killing that make the precise characterization of the type of cell death less important than the quantification of cell survival using clonogenic endpoints when evaluating cancer therapies: We believe that the above considerations are succinctly summarized by the Nomenclature Committee on Cell Death 2012 (6): “During the process of functional characterization, great attention should be paid to ensure that genetic and chemical interventions truly modify the incidence of cell death (as assessed by clonogenic cell survival), rather than activate alternative lethal pathways.”See the Response, p. 3682No potential conflicts of interest were disclosed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".