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Record W4285686073 · doi:10.4103/ijmr.ijmr_4454_20

Updates on systemic therapy for cervical cancer

2021· review· en· W4285686073 on OpenAlexaff
Paul Gopu, Febin Antony, Sunu Cyriac, Katherine Karakasis, Amit M. Oza

Bibliographic record

VenueThe Indian Journal of Medical Research · 2021
Typereview
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineBevacizumabCervical cancerRadiation therapyChemotherapyOncologyDiseaseInternal medicineCancerTargeted therapyIntensive care medicineQuality of life (healthcare)Modalities

Abstract

fetched live from OpenAlex

Cervical cancer is one of the most common cancers in the world both in terms of incidence and mortality, more so important in low- and middle-income countries. Surgery and radiotherapy remain the backbone of treatment for non-metastatic cervical cancer, with significant improvement in survival provided by addition of chemotherapy to radiotherapy. Survival as well as quality of life is improved by chemotherapy in metastatic disease. Platinum-based chemotherapy with/without bevacizumab is the mainstay of treatment for metastatic disease and has shown improvement in survival. The right combinations and sequence of treatment modalities and medicines are still evolving. Data regarding the molecular and genomic biology of cervical cancer have revealed multiple potential targets for treatment, and several new agents are presently under evaluation including targeted therapies, immunotherapies and vaccines. This review discusses briefly the current standards, newer updates as well as future prospective approaches in systemic therapies for cervical 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.003

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.

Opus teacher head0.272
GPT teacher head0.544
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations69
Published2021
Admission routes1
Has abstractyes

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Same venueThe Indian Journal of Medical ResearchSame topicEndometrial and Cervical Cancer TreatmentsFrench-language works237,207