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Record W7073714930

Research Priorities for Endometriosis.

2017· article· en· W7073714930 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2017
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMEDLINEResearch ethicsTranslational researchStatement (logic)Alternative medicine
DOInot available

Abstract

fetched live from OpenAlex

The 3rd International Consensus Workshop on Research Priorities in Endometriosis was held in São Paulo on May 4, 2014, following the 12th World Congress on Endometriosis. The workshop was attended by 60 participants from 19 countries and was divided into 5 main sessions covering pathogenesis/pathophysiology, symptoms, diagnosis/classification/prognosis, disease/symptom management, and research policy. This research priorities consensus statement builds on earlier efforts to develop research directions for endometriosis. Of the 56 research recommendations from the 2011 meeting in Montpellier, a total of 41 remained unchanged, 13 were updated, and 2 were deemed to be completed. Fifty-three new research recommendations were made at the 2014 meeting in Sao Paulo, which in addition to the 13 updated recommendations resulted in a total of 66 new recommendations for research. The research recommendations published herein, as well as those from the 2 previous papers from international consensus workshops, are an attempt to promote high-quality research in endometriosis by identifying and agreeing on key issues that require investigation. New areas included in the 2014 recommendations include infertility, patient stratification, and research in emerging nations, in addition to an increased focus on translational research. A revised and updated set of research priorities that builds on this document will be developed at the 13th World Congress on Endometriosis to be held on May 17-20, 2017, in Vancouver, British Columbia, Canada.

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)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.068
GPT teacher head0.330
Teacher spread0.262 · 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.

Study designNot applicable
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

Citations9
Published2017
Admission routes1
Has abstractyes

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