MétaCan
Menu
Back to cohort
Record W2887996065 · doi:10.4236/ojog.2018.810085

FSH/LH Ratio as a Predictor of the IVF Outcome in Young Women

2018· article· en· W2887996065 on OpenAlexaff
Eman Shaeer, Ahmed M. Maged, Dina Shaheen, Hala Gomaa

Bibliographic record

VenueOpen Journal of Obstetrics and Gynecology · 2018
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsCReATe Fertility Centre
Fundersnot available
KeywordsMedicineIn vitro fertilisationLuteinizing hormoneInfertilityFollicle-stimulating hormonePregnancy rateFertilityPregnancyGynecologyGonadotropinHuman fertilizationAndrologyHormoneInternal medicineBiologyPopulation

Abstract

fetched live from OpenAlex

In Vitro Fertilization (IVF) is the treatment for many causes of infertility. Many studies were done to investigate different factors that can affect the success rate. This study was conducted to evaluate if cycle day 3 (CD3) follicle-stimulating hormone (FSH)/luteinizing hormone (LH) ratio can be a predictor for the IVF outcome in young sub-fertile females ≤ 35 years with normal baseline FSH. This is a retrospective case control study conducted at the Centre of Fertility and Andrology Care (CFAC) in Egypt where 235 sub-fertile women underwent IVF. Patients were grouped based on CD3 FSH/LH ratio. Group A consisted of ≤35-year-old women with FSH/LH ratio ≤35-year-old women with FSH/LH ratio ≥ 2. The primary outcomes include the fertilization rate, implantation rate and the clinical pregnancy rate. The secondary outcomes include duration and the total dose of gonadotrophin used. We found that, there was no significant difference in the total dose of gonadotropin used during the IVF cycle. Also, there was no significant difference in the number of retrieved and fertilized oocytes and the number of good embryos. Clinical pregnancy rate was the same in both groups. In conclusion, in patients younger than 35 years, CD3 FSH/LH ratio is not a predictor for IVF outcome.

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)0.007
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 score0.049
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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)0.0010.000

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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueOpen Journal of Obstetrics and GynecologySame topicOvarian function and disordersFrench-language works237,207