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Record W1993762970 · doi:10.1097/aog.0b013e31818bba31

Preservation of Female Fertility

2008· review· en· W1993762970 on OpenAlexaff
Togas Tulandi, Jack Y.J. Huang, Seang Lin Tan

Bibliographic record

VenueObstetrics and Gynecology · 2008
Typereview
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineFertilityFertility preservationCryopreservationInfertilityEmbryo cryopreservationPremature ovarian failureSterilityGynecologyPremature ovarian insufficiencyVitrificationOvarian tissue cryopreservationAndrologyObstetricsEmbryoPregnancyPopulationInternal medicineBiology

Abstract

fetched live from OpenAlex

Chemotherapy and radiation treatment for malignancies or other conditions such as hematologic and autoimmune disorders, have resulted in improved survival rates but may lead to sterility. Women who postpone conception until late reproductive years are also at increased risk to become infertile. The purpose of our review is to evaluate advances and techniques for fertility preservation. We performed a literature search using the keywords fertility preservation, vitrification, oocytes, embryo, ovarian cryopreservation, and ovarian suspension and conducted the search in MEDLINE, EMBASE, and the Cochrane Database of systematic reviews. The results show that today, it is possible to cryopreserve oocytes, embryos, or ovarian tissue. The most commonly used technique remains embryo cryopreservation. Another improvement is the development of vitrification or rapid freezing technique. For women undergoing local pelvic radiation, one should consider ovarian suspension. Medical professionals, patients, and their families should be aware that in some conditions, the reproductive function can be preserved. Although one cannot guarantee future fertility, a realistic hope for women at risk of having premature ovarian failure can now be offered.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.090
GPT teacher head0.346
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2008
Admission routes1
Has abstractyes

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