Evaluating Elective Egg Freezing Consent Materials From Canadian Fertility Clinics
Bibliographic record
Abstract
The use of elective egg freezing (EEF) has rapidly increased in recent years. Despite its popularity, scholars have documented a host of concerns in relation to the use of this technology, especially given aggressive advertising of EEF by the fertility industry as “insurance” and lack of data about success rates. Informed consent processes, and informed consent materials, are particularly important in situations like EEF where healthy people are undergoing interventions that are neither life nor health preserving. Despite these concerns necessitating a rigorous consent process, no research in Canada has explored consent processes surrounding EEF at Canadian fertility clinics to assess whether they are meeting this heightened standard. In this paper, I analyze EEF consent forms and accompanying materials collected from 11 Canadian fertility clinics. I assess the extent to which the consent forms and accompanying materials adhere to a seven-part ethical framework for minimum standards of disclosure for EEF that I argue is supported by Canadian legislation, regulations, guidelines, case law and Health Professions Appeal and Review Board (HPARB) decisions on informed consent. Ultimately, I found that consent processes for EEF rely on an unstandardized patchwork of information sources. Clinic consent forms and accompanying materials do not adhere to minimum elements of disclosure for consent for EEF and, in many cases, do not adhere to existing law, guidelines and HPARB decisions governing these consent processes in Canada. I argue that an overhaul is needed to ensure that people freezing their eggs have the basic information they need to make informed decisions and make recommendations for how to further regulate EEF consent processes.
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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 | 0.064 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".