New clinical and experimental approaches for studying tumor dormancy: does tumor dormancy offer a therapeutic target?
Bibliographic record
Abstract
Tumor dormancy is a significant clinical problem. Primary treatment of a cancer may be apparently successful, and yet the tumor may recur either locally or as distant metastases years or even decades later. The ability to predict which patients are likely to develop recurrences is imprecise, relying on probabilities of recurrence based on features of the primary cancer. This uncertainty presents clinical challenges regarding who to treat and how, in order to prevent recurrence after periods of dormancy. Recent clinical trials in breast cancer support the idea that some patients may harbor tumor cells that are capable of forming late-developing metastases years after removal of the primary tumor, and that these dormant cancer cells may in some cases be effectively treated with long-term therapy. Advances in experimental studies of tumor dormancy are shedding light on the nature of dormancy, and are providing both new technologies and conceptual approaches for studying tumor dormancy. A better understanding of mechanisms responsible for tumor dormancy and recurrence will be important for improving care of patients at risk for late-developing metastases.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".