Freezing the Biological Clock: A Viable Fertility Preservation Option for Young Singaporean Women?
Bibliographic record
Abstract
In March 2012, an article in The Straits Times entitled ‘Freezing eggs could reverse falling birth rate’ suggested that employing the latest oocyte cryopreservation techniques could both foster individual women’s reproductive autonomy and impact Singapore’s fertility rate, which in recent years has consistently been among the world’s lowest. The article cited both local and international fertility specialists’ approval of elective oocyte cryopreservation for young women wishing to protect their reproductive potential against ageing and as a potential antidote to the contemporary ‘delay and defer’ model of family-building. Later in 2012, the Ministry of Health announced a review of oocyte cryopreservation policy taking into account related medical, scientific and ethical issues, while the Singapore College of Obstetricians and Gynaecologists endorsed oocyte cryopreservation as an “important, safe and efficient technology”. This paper outlines and analyses the arguments and empirical evidence used both to support and oppose offering elective oocyte cryopreservation as a routine fertility service, before concluding that this remains unjustifiable on the basis of insufficient evidence of its clinical efficacy and safety as regards either pregnancy rates or birth outcomes. If it is to be made available at all for these reasons in Singapore, it should be subjected to rigorous clinic-specific evaluation in accordance with accepted clinical and ethical norms. Key words: Elective oocyte cryopreservation, Outcomes
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".