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Record W2560073692 · doi:10.1080/21678707.2017.1264300

Rare tumors in gynaecological cancers and the lack of therapeutic options and clinical trials

2016· article· en· W2560073692 on OpenAlexaff
Victoria Mandilaras, Katherine Karakasis, Blaise Clarke, Amit M. Oza, Stéphanie Lheureux

Bibliographic record

VenueExpert Opinion on Orphan Drugs · 2016
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineBiobankClinical trialIntensive care medicineExpert opinionIncidence (geometry)CancerPathologyInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

Introduction: Up to 50% of gynecological cancers can be considered rare according to the Surveillance of Rare Cancers in Europe (RARECARE) consortium definition of an incidence of less than 6 cases per 100 000 people. These cancers usually have a poor prognosis as they are often delayed in their diagnosis and treatment due to the lack of knowledge.Areas covered: This review briefly addresses the current state of management and the lack of effective treatment strategies for the most commonly seen rare gynecological malignancies. It also highlights the challenges surrounding attempts to harmonize treatment practices and the role of the international medical community.Expert opinion: Given their rarity, biological and clinical data are lacking for many gynecological cancers. Current efforts are on-going to improve care of these patients, including the development of international consortia, prospective databases with biobanking, acceptance of novel clinical trial design and education of the medical field as well as improvement of patient awareness.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.157
GPT teacher head0.450
Teacher spread0.293 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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