The disconnect: infertility patients' information and the role they wish to play in decision making.
Bibliographic record
Abstract
OBJECTIVE: To determine the preferred role in medical decision making of women undergoing fertility treatments and to establish whether their knowledge of treatments is adequate to inform their choices. METHODS: Self-report survey of 404 women undergoing fertility treatments in 2 university hospitals and a private fertility clinic in Canada. RESULTS: The women had been in fertility treatment for 2.3 +/- 2.6 years; 67.8% reported taking fertility drugs. Most (61.7%) women wanted to share knowledge equally with their doctors about possible fertility treatments. However, about half wanted to decide alone or mostly by themselves about the acceptability of treatment risks and benefits (56%), what treatments should be selected (49.8%), and when to conclude treatments (54.3%). In addition, 55.1% of the women did not know their personal eventual chances of pregnancy with fertility treatment or thought it was 50% or greater. Over half of the women (57.2%) who had taken fertility drugs were unaware of a possible link between fertility drugs and increased ovarian cancer risk. The majority of women (61.8%) who knew of this possible association reported that they learned about it from the print media. Women who knew of the association had a poor understanding of the strength of the evidence or the ability to detect or treat ovarian cancer successfully, and 88.3% thought they could reduce cancer risk by following their doctors' advice. CONCLUSIONS: Despite these women's wishes to actively participate in fertility treatment decisions, they lacked the necessary information to do so meaningfully. Public health policymakers, professional and advocacy organizations, physicians, other healthcare providers, and women themselves must find ways to improve the general public's and patients' understanding about fertility treatment outcomes and risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| 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) | 0.000 | 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 teacher head, 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".