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Record W2162472136 · doi:10.1111/his.12419

Assignment of primary site in high‐grade serous tubal, ovarian and peritoneal carcinoma: a proposal

2014· review· en· W2162472136 on OpenAlexaff
Naveena Singh, C. Blake Gilks, Nafisa Wilkinson, W. Glenn McCluggage

Bibliographic record

VenueHistopathology · 2014
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsFallopian tubeSerous carcinomaPeritoneumSerous fluidMedicineOvaryOvarian cancerStage (stratigraphy)Serous membraneOvarian carcinomaCarcinomaPathologyGynecologyOncologyCancerInternal medicineBiology

Abstract

fetched live from OpenAlex

The revised FIGO 2013 staging for carcinomas of the ovary, fallopian tube and peritoneum has introduced a single system for tumours originating at these sites. The system requires pathologists to assign a primary site (ovary, tube or peritoneum), but does not provide guidance to aid this assignment. This is particularly problematic in cases of advanced-stage (stage II or greater) high-grade serous carcinoma (HGSC), where there is commonly involvement of two or more sites by tumour, and practice among pathologists in determining where a tumour has arisen varies widely. This has significant implications for recording of tumour incidence and mortality, data collection by cancer registries, and entry into clinical trials. We propose guidelines for assigning the primary site of HGSC based on careful macroscopic and histological assessment. The use of these guidelines, in conjunction with the new FIGO staging system, is intended to act as an impetus to promote debate and provide a uniform and consistent approach in assigning primary tumour site which will facilitate comparison of data between centres.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.005
Science and technology studies0.0010.003
Scholarly communication0.0020.004
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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

Citations82
Published2014
Admission routes1
Has abstractyes

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