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Record W2730648198

Controlled Ovarian Hyperstimulation with Intrauterine Insemination Is More Successful After r-hCG Administration Than Spontaneous LH Surge.

2017· article· en· W2730648198 on OpenAlexaff
Evan Taerk, Edward G. Hughes, Cassandra Greenberg, Michael S. Neal, Shilpa Amin, Mehrnoosh Faghih, Megan Karnis

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsControlled ovarian hyperstimulationMedicineInfertilityPregnancyPregnancy rateGynecologyGonadotropinLive birthAndrologyInternal medicineHormoneBiology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this study was to evaluate whether clinical pregnancy rate is affected by timing intrauterine insemination (IUI) according to serum LH surge, r-hCG trigger, or a combination of LH surge and r-hCG trigger in controlled ovarian hyperstimulation (COH) cycles for patients with a variety of infertility etiologies. METHODS: The last 365 consecutive COH-IUI cycles performed at ONE Fertility Burlington in 2014 were reviewed and categorized according to method of IUI timing. Associations between categorical variables were analyzed using a combination of Chi-square and Fisher's Exact tests, and between continuous variables using independent sample t-tests and logistic regression to a level of significance of p<0.05. RESULTS: . 2.1%, p=0.01). CONCLUSION: In subfertile couples undergoing COH-IUI, r-hCG administration was associated with an increased clinical pregnancy rate compared with spontaneous serum LH surge. When r-hCG was administered concomitantly with a serum LH surge, this benefit was amplified. The effect appears to be of particular importance in r-FSH-medicated cycles.

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 score0.500
Threshold uncertainty score0.657

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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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