Uninformed Decisions? The Online Presentation of Success and Failure of IVF and Related Methods on German IVF Centre Websites
Bibliographic record
Abstract
Background: Patients increasingly use the internet as a source of medical information before initial contact with doctors and during treatment. This applies to reproductive medicine too, where the internet could offer patients the chance to inform themselves in advance about specific procedures and the treatment centres that offer them. In this way it could potentially contribute to informed patient decision-making. This article analyses the web presence of German fertility treatment centres with respect to the provision of information on success rates, risks and side effects of treatment. Methods: Analysis of published success rates and information on the risks and adverse effects of IVF and related methods on German IVF centre websites. Results: Over half of the 129 centres (62.02 %) state a general success rate or their own institutionʼs success rate. Less than a quarter (24.03 %) states their own institutionʼs pregnancy rate and only 7.75 % their own birth rate. The published success rates are mostly pregnancy rates (pregnancy per embryo transfer), which by definition are higher than baby take-home-rates creating unrealistic expectations. Only 61 centres (47.29 %) mention risks and side effects of the procedures offered, and that in varying detail. Only 7 centres (5.43 %) provide information on the risk of psychological stress associated with unsuccessful fertility treatment. Conclusion: There is insufficient opportunity for women and their partners to inform themselves adequately on the internet in advance of treatment about available treatment methods, their success rates and associated risks/side effects; this applies both to specific facilities as well as to the procedures in general. In contrast to other countries, in Germany there is a lack of discussion on content requirements for fertility treatment facility websites.
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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 | 0.012 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".