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Record W2160945991 · doi:10.1155/2014/890141

Phytochemicals: A Multitargeted Approach to Gynecologic Cancer Therapy

2014· review· en· W2160945991 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioMed Research International · 2014
Typereview
Languageen
FieldMedicine
TopicCancer Mechanisms and Therapy
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersMinistry of Education, Science and TechnologyNational Research Foundation of KoreaCanadian Institutes of Health ResearchNational Research Foundation
KeywordsRadiation therapyMedicineCancerChemotherapyOncologyQuality of life (healthcare)Downregulation and upregulationBioinformaticsInternal medicineCancer researchBiologyGene

Abstract

fetched live from OpenAlex

Gynecologic cancers constitute the fourth most common cancer type in women. Treatment outcomes are dictated by a multitude of factors, including stage at diagnosis, tissue type, and overall health of the patient. Current therapeutic options include surgery, radiotherapy, and chemotherapy, although significant unmet medical needs remain in regard to side effects and long-term survival. The efficacy of chemotherapy is influenced by cellular events such as the overexpression of oncogenes and downregulation of tumor suppressors, which together determine apoptotic responses. Phytochemicals are a broad class of natural compounds derived from plants, a number of which exhibit useful bioactive effects toward these pathways. High-throughput screening methods, rational modification, and developments in regulatory policies will accelerate the development of novel therapeutics based on these compounds, which will likely improve overall survival and quality of life for patients.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.374
GPT teacher head0.560
Teacher spread0.186 · 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