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Record W2106183217 · doi:10.1093/humupd/dml002

Endocrine signaling in ovarian surface epithelium and cancer

2006· review· en· W2106183217 on OpenAlexaff
Peter C. K. Leung, Jung‐Hye Choi

Bibliographic record

VenueHuman Reproduction Update · 2006
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
Fundersnot available
KeywordsOvarian cancerCancerEndocrine systemBiologyCancer researchInternal medicineHormoneEndocrinologyOncologyMedicine

Abstract

fetched live from OpenAlex

Ovarian cancer is the sixth most common cancer and the fifth leading cause of cancer-related death among women in developed countries. Greater than 85% of human ovarian cancer arises within the ovarian surface epithelium (OSE), with the remainder derived from granulosa cells or, rarely, stroma or germ cells. The pathophysiology of ovarian cancer is the least understood among all major human malignancies because of a poor understanding of the aetiological factors and mechanisms of ovarian cancer progression. There is increasing evidence suggesting that several key reproductive hormones, such as GnRH, gonadotrophins and sex steroids, regulate the growth of normal OSE and ovarian cancer cells. The objective of this review was to highlight the effects of these endocrine factors on ovarian cancer cell growth and to summarize the signalling mechanisms involved in normal human OSE and its neoplastic counterparts.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.062
GPT teacher head0.373
Teacher spread0.310 · 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

Citations78
Published2006
Admission routes1
Has abstractyes

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