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Record W4253697418 · doi:10.1093/humrep/deg053

Oocytes from younger women with increased serum FSH are superior to those from older women with hypergonadotrophism

2003· article· en· W4253697418 on OpenAlexaff
Yacoub Khalaf, Tarek El‐Toukhy

Bibliographic record

VenueHuman Reproduction · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSexual Differentiation and Disorders
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineFollicle-stimulating hormoneGonadotropinGynecologyAndrologyLuteinizing hormoneEndocrinologyHormone

Abstract

fetched live from OpenAlex

Dear Sir, We thank Dr Check for his interest in our study and the issues he raised in his letter. Firstly, Dr Check raised the issue whether young women are protected from the adverse effects of reduced ovarian reserve. He made reference to a study produced in 1997 (Check et al., 1998), where the authors evaluated pregnancy and on‐going pregnancy rates in a small number of women (n = 45) with raised basal FSH levels treated without the use of assisted reproductive technology (ART). It is fundamentally incorrect to compare data from IVF‐treated patients with those from the general subfertility population, when multifollicular development is not as important for achieving a pregnancy. Furthermore, no distinction was made in that study (Check et al., 1998) between patients ≤30 years and those between 31–39 years. In a larger study, life table analysis was used to evaluate the effect of age on pregnancy rates achieved in a group of women with reduced ovarian reserve, confirmed by an abnormal clomiphene challenge test (Scott et al., 1995). The study showed no age‐related differences in pregnancy rates, which were similarly low in all age groups studied. Within the field of IVF, in addition to our study, many studies (Scott et al., 1989; Toner et al., 1991; Margarelli et al., 1996) have emphasized the importance of ovarian age, over chronological age alone, in predicting treatment 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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.727

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)0.0010.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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 designBench or experimental
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

Citations0
Published2003
Admission routes1
Has abstractyes

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