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Record W2323089456 · doi:10.1097/pgp.0000000000000270

Adopting a Uniform Approach to Site Assignment in Tubo-Ovarian High-Grade Serous Carcinoma

2016· article· en· W2323089456 on OpenAlexaff
Naveena Singh, C. Blake Gilks, Lynn Hirshowitz, Nafisa Wilkinson, W. Glenn McCluggage

Bibliographic record

VenueInternational Journal of Gynecological Pathology · 2016
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsTerminologySerous carcinomaSerous fluidOvarian carcinomaFallopian tubeDiseaseComputer scienceProtocol (science)MedicineOvarian cancerGynecologyPathologyInternal medicineCancerLinguistics

Abstract

fetched live from OpenAlex

There is currently sufficient evidence that nonuterine high-grade serous carcinoma (HGSC) originates in the fallopian tube in the majority of cases, but this is not uniformly reflected in our diagnostic terminology. This is because there remains wide variation in awareness and acceptance of this evidence, which conflicts with traditional views on origin. Accurate disease classification is fundamental to routine clinical practice and research, particularly at a time when exciting new approaches to therapy, early detection, and prevention are appearing on the horizon. We feel the time has come to minimize individual and institutional variations in practice, and agree on an evidence-based approach to uniform terminology and primary site assignment. In this paper we put forward a proposal for a unified approach based on published research evidence and discuss the reasons why it is vital to agree on a uniform protocol. We propose the term "Tubo-ovarian HGSC" in preference to "pelvic" or "Müllerian," as it accurately reflects the origin of this disease in the vast majority of cases, and is unambiguous, distinguishing it clearly from uterine serous carcinoma and ovarian low-grade serous carcinomas. A detailed protocol for primary site assignment is presented for different scenarios, which is easy to follow and has been developed with a view to promoting a uniform approach worldwide.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations50
Published2016
Admission routes1
Has abstractyes

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