Knowledge and attitudes regarding elective oocyte cryopreservation in undergraduate and medical students
Bibliographic record
Abstract
To assess knowledge and attitudes regarding elective oocyte cryopreservation among female undergraduate students (UG) and medical students (MS) in Eastern Virginia. An anonymous cross-sectional study surveying female UG at a local university and MS at our academic medical center in May of 2017. The survey contained questions on demographic information, interest in fertility preservation, and knowledge about age related changes in fertility. There were 74 of 102 female UG and 95 of 117 female MS who responded, for a response rate of 73 and 81% respectively. UG were significantly younger than MS (21.4 vs 26.8, p < 0.001). Further, UG generally planned on conceiving at a younger age than MS (age 26–30 vs 31–35), and favored younger ages to consider oocyte cryopreservation (age 26–30 vs 31–35). Only a minority of both UG and MS were willing to undergo egg freezing at the current price of approximately $10,000 (15% vs 26% respectively, p = 0.044). Moreover, 73% of students overall responded that they would be more likely to freeze oocytes if their employer paid. Notably, both UG and MS underestimated age of fertility decline. Both UG and MS revealed a need for education on age-related changes in fertility. Most UG and MS would not undergo elective oocyte cryopreservation at the present cost but would consider it at a lower cost.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".