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Record W4308733170 · doi:10.1097/rd9.0000000000000053

Medical treatments of endometriosis: a review

2022· review· en· W4308733170 on OpenAlexaff
Bahi Elbasueny, Maya Geerts, Emily C. Yang, Catherine Allaire, Paul J. Yong, Mohamed A. Bedaiwy

Bibliographic record

VenueReproductive and Developmental Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEndometriosisMedicinePelvic painMedical literatureIntensive care medicineGynecologySurgeryPathology

Abstract

fetched live from OpenAlex

Endometriosis is a chronic gynecological disorder that affects approximately 10% of women of reproductive age. Most medical treatments used today for endometriosis pain are hormonal therapies, which are not an option for those trying to conceive and are not tolerated by a subset of patients due to side effects. In this article, we offer a comprehensive review of current and investigational medical therapeutic options used to treat endometriosis pain, as well as a symptom-based systematic approach for patients with painful endometriosis. We have also included recommendations for research to enhance the evolution of novel therapeutic options. A thorough literature search was carried out, and the data were synthesized using a synthesis matrix that classifies and categorizes various arguments.

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)0.002
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.119
GPT teacher head0.421
Teacher spread0.302 · 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 designOther design
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

Citations6
Published2022
Admission routes1
Has abstractyes

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