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Record W2146583760 · doi:10.1186/1477-7827-10-55

Addition of low dose hCG to rFSh benefits older women during ovarian stimulation for IVF

2012· article· en· W2146583760 on OpenAlexaff
Hala Gomaa, Robert F. Casper, Navid Esfandiari, Paul Chang, Yaakov Bentov

Bibliographic record

VenueReproductive Biology and Endocrinology · 2012
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai HospitalCanadian Institute for Advanced Research
Fundersnot available
KeywordsMedicineReproductive medicineOvarian reserveStimulationGynecologyOvulation inductionAndrologyEndocrinologyInfertilityObstetricsPregnancyOvulationHormoneBiology

Abstract

fetched live from OpenAlex

BACKGROUND: To compare the outcome of IVF cycles in women receiving controlled ovarian stimulation with recFSH or recFSH plus low dose hCG. METHODS: A retrospective case control study, performed at a private practice affiliated with an academic institute. Patients were infertile women who were treated with IVF/ICSI and controlled ovarian stimulation in a long GnRH agonist protocol using either low dose hCG in addition to recFSH [N = 88] or recFSH alone [N = 99]. Primary outcomes were mean FSH dose, number of mature eggs, number of fertilized eggs, and serum levels of estradiol. Secondary outcomes were endometrial thickness, cycle cancellations and pregnancy rates. RESULTS: A significant increase in number of mature and fertilized eggs was observed in women over 40 years of age using low dose hCG in addition to recFSH. The estradiol level was significantly higher on the day of hCG administration and the serum level of FSH on cycle day 7 and on the day of hCG administration were lower. CONCLUSION: Addition of low dose hCG to recFSH compared with recFSH alone significantly modified cycle characteristics in patients >/= 40 years and could be of potential benefit for IVF cycles in older infertile women.

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 score0.418
Threshold uncertainty score0.425

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 head0.020
GPT teacher head0.290
Teacher spread0.270 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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