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Record W2410672923

The benefits of other treatments than in vitro fertilization to aid conception in minimal and mild endometriosis.

2016· article· en· W2410672923 on OpenAlexaff
Annie Leung, Michael H. Dahan

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsMedicineOvulation inductionLetrozoleOvulationPregnancyInfertilityEndometriosisGynecologyObstetricsIn vitro fertilisationIntrauterine inseminationPregnancy rateFertilityUnexplained infertilityInseminationHuman fertilizationAndrologyHormonePopulationInternal medicineSperm
DOInot available

Abstract

fetched live from OpenAlex

The treatment of minimal or mild endometriosis prior to non-in-vitro fertilization (IVF) assisted reproduction to improve pregnancy outcomes is controversial. Ovulation suppression may be offered to women who do not wish to conceive to suppress advancement of the disease. There is little evidence to suggest improvements in fertility associated ovarian suppression prior to non-IVF infertility treatments. The use of intrauterine insemination without ovulation induction offers little benefit, with low pregnancy rates in most studies. Surgical ablation seems to improve outcomes when other care will not be delivered. Although controversial, surgical ablation before ovulation induction may offer benefit but further studies would be helpful. Ovulation induction seems to increase pregnancy rates and either letrozole or clomiphene citrate should be considered as first line options. If pregnancy does not occur with three months of ovulation induction, based on dropping success rates with further cycles of ovarian stimulation, IVF should be offered.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.042
GPT teacher head0.287
Teacher spread0.245 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicEndometriosis Research and Treatment→French-language works237,207→