Continuous Docetaxel Chemotherapy Improves Therapeutic Efficacy in Murine Models of Ovarian Cancer
Bibliographic record
Abstract
Ovarian cancer is known as the silent killer for being asymptomatic until late stages. Current first-line treatment consists of debulking surgery followed by i.v. chemotherapeutics administered intermittently, which leads to insufficient drug concentrations at tumor sites, accelerated tumor proliferation rates, and drug resistance, resulting in an overall median survival of only 2 to 4 years. For these reasons, more effective treatment strategies must be developed. We have investigated a localized, continuous chemotherapy approach in tumor models of human and murine ovarian cancers using the antineoplastic agent docetaxel. We show here that continuous docetaxel therapy is considerably more efficacious than intermittent therapy, resulting in a greater decrease in tumor burden and ascites fluid accumulation. Immunohistochemical analyses show that continuous chemotherapy abrogates tumor cell proliferation and angiogenesis to the tumor microenvironment, leading to greater tumor cell death than intermittent docetaxel therapy. Overall, our results show greater therapeutic advantages of continuous over intermittent chemotherapy in the treatment of ovarian 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.000 |
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".