Metronomic Chemotherapy for Treatment of Metastatic Disease: From Preclinical Research to Clinical Trials
Bibliographic record
Abstract
Metastasis is the culmination of tumor progression and remains both the primary cause of mortality for cancer patients, as well as the most challenging aspect of cancer therapy. The main systemic treatment of metastatic disease – chemotherapy – was designed with the aim of killing as many tumor cells as possible by using cytotoxic agents at the maximum tolerated dose (MTD) [1, 2]. However, such regimens are associated with a number of inherent limitations. For instance, the administration of high dosages of chemotherapeutic agents results in toxicity, which is sometimes serious in nature (e.g., myelosuppression and damage to intestinal mucosa). As such, this requires the incorporation of prolonged breaks (often, three weeks) between treatments to allow recovery of depleted cells (e.g., neutrophils from bone marrow progenitors) [3]. Unfortunately, these breaks also allow for tumor regrowth to occur such that any regressions achieved by MTD therapy are usually only transitory [2]. In addition, due to the inherent ability of tumor cells to acquire resistance to cytotoxic agents, most MTD therapies eventually fail, resulting in resumption of disease progression. Overall, most MTD therapies have proven generally ineffective or of modest (mostly palliative) benefit in the treatment of advanced metastatic disease [3]. Clearly, a rethinking of approaches to treat metastatic disease is in order. This involves, at least in part, a reexamination of the dosing schedule regimens of chemotherapeutic agents that are best suited to treat this most intractable aspect of the pathology of cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".