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Record W2138124620 · doi:10.1158/1535-7163.mct-10-0249

Continuous Docetaxel Chemotherapy Improves Therapeutic Efficacy in Murine Models of Ovarian Cancer

2010· article· en· W2138124620 on OpenAlexafffund
Raquel De Souza, Payam Zahedi, Eduardo H. Moriyama, Christine Allen, Brian C. Wilson, Micheline Piquette‐Miller

Bibliographic record

VenueMolecular Cancer Therapeutics · 2010
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDocetaxelOvarian cancerChemotherapyMedicineDebulkingOncologyInternal medicineCancerAngiogenesisTumor microenvironmentCancer researchPharmacology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.303
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations42
Published2010
Admission routes2
Has abstractyes

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