Cancer Stem Cells Promote Tumor Vascular Development – Response
Bibliographic record
Abstract
If we understand correctly the arguments outlined by Bonnefoix and Callanan, they are suggesting that the limiting dilution transplantation assay (LDTA) results that we presented do not fit the Single-Hit Poisson Model. Although it is correct that the fact that our LDTA results do not fit the Single-Hit Poisson Model would prevent us from using Poisson statistics to estimate the exact frequency of cancer stem cells in each culture, we see no reason why this would prevent us from drawing a conclusion about the relative tumor initiating capacity in each culture (which, incidentally, was the only direct conclusion we made in our article based on the LDTA). Such a conclusion does not, to our knowledge, assume single-hit statistics, and it still allows for the possibility that tumor initiation by cancer stem cells is not necessarily a single-hit event (which is probably the case, especially in a xenograft assay). Therefore, we think it may be inappropriate to state categorically that any comparison between Ser+ and SFS LDTAs is precluded. Disclosure of Potential Conflicts of Interest No 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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".