Individualized Ovarian Stimulation in Patients with Advanced Maternal Age and Premature Ovarian Aging
Bibliographic record
Abstract
Because studies of older and, otherwise, unfavorable patients going through in vitro fertilization (IVF) treatments with own (autologous) oocytes are sparse, we here present to a large degree the subjective experience of only one fertility center in New York City, which as of this point contributed a majority of published studies on this subject. As US national IVF data registries by the Center for Disease Control and Prevention (CDC) and the Society for Assisted Reproductive Technologies (SART) demonstrate, this center serves the by-far oldest patient population among over 500 reporting US IVF centers and, therefore, likely the oldest patient population of any IVF center in the world. While the median age of all US centers reporting to the CDC in 2016 was 36 years, this center’s median age was 42 years in 2016 and 43 years in 2017 and 2018. Over 90 percent of the center’s new patients in recent years reported prior failed IVF cycles, often at multiple centers. Over half of the center’s patients are so-called long-distance patients from outside the larger New York City Tri-State area, many from Canada and overseas. Finally, in excess of 95 percent of the center’s patients suffer from LFOR, which means that even younger patients usually demonstrate abnormally high age-specific follicle-stimulating hormone (FSH) and abnormally low anti-Müllerian hormone (AMH). This center, thus, overall, likely, serves the poorest-prognosis patient population of any IVF center in the world.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".