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Revisiting the Role of Radiation Treatment for Non-serous Subtypes of Epithelial Ovarian Cancer

2013· review· en· W4247326642 on OpenAlexaff
Grégoire Thomas

Bibliographic record

VenueAmerican Society of Clinical Oncology Educational Book · 2013
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsSerous fluidMedicineOvarian cancerOncologyRadiation therapyChemotherapyClear cellInternal medicineSerous ovarian cancerDiseaseCancerStage (stratigraphy)CarcinomaBiology

Abstract

fetched live from OpenAlex

Except for its palliative use, radiation has been largely abandoned in the management of ovarian cancers because of the recognized efficacy of chemotherapy agents. Whole abdominal irradiation (WAR), however, has been shown to be of adjuvant and curative value in ovarian cancer with microscopic or minimal residual disease in the pelvis, the so-called “intermediate risk group.” Recent hypothesis generating data from the use of adjuvant radiation following adjuvant chemotherapy in ovarian cancer has shown an incremental survival benefit for the rarer non-serous ovarian subtypes including clear cell, endometrioid, and mucinous. No incremental benefit was observed for the more common serous subtype. A retrospective examination of early trials using WAR as the sole postoperative treatment in ovarian cancer has determined that the majority of patients in these studies and cured by radiation actually had the non-serous subtypes. The recognition that the non-serous subtypes differ from the serous cancers in their stage of presentation, their molecular characteristics, their response to classic chemotherapy, and their outcomes suggest the non-serous subtypes should be treated as rare and different cancers. In addition to specific targeting therapies that may be developed, radiation should be reconsidered as part of the treatment armamentarium for these diseases.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.093
GPT teacher head0.481
Teacher spread0.388 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations6
Published2013
Admission routes1
Has abstractyes

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