Worldwide decline of IVF birth rates and its probable causes
Bibliographic record
Abstract
With steadily improving pregnancy and live birth rates, IVF over approximately the first two and a half decades evolved into a highly successful treatment for female and male infertility, reaching peak live birth rates by 2001-2002. Plateauing rates, thereafter, actually started declining in most regions of the world. We here report worldwide IVF live birth rates between 2004 and 2016, defined as live births per fresh IVF/ICSI cycle started, and how the introduction of certain practice add-ons in timing was associated with changes in these live birth rates. We also attempted to define how rapid worldwide 'industrialization' (transition from a private practice model to an investor-driven industry) and 'commoditization' in IVF practice (primary competitive emphasis on revenue rather than IVF outcomes) affected IVF outcomes. The data presented here are based on published regional registry data from governments and/or specialty societies, covering the USA, Canada, the UK, Australia/New Zealand (combined), Latin America (as a block) and Japan. Changes in live birth rates were associated with introduction of new IVF practices, including mild stimulation, elective single embryo transfer (eSET), PGS (now renamed preimplantation genetic testing for aneuploidy), all-freeze cycles and embryo banking. Profound negative associations were observed with mild stimulation, extended embryo culture to blastocyst and eSET in Japan, Australia/New Zealand and Canada but to milder degrees also elsewhere. Effects of 'industrialization' suggested rising utilization of add-ons ('commoditization'), increased IVF costs, reduced live birth rates and poorer patient satisfaction. Over the past decade and a half, IVF, therefore, has increasingly disappointed outcome expectations. Remarkably, neither the profession nor the public have paid attention to this development which, therefore, also has gone unexplained. It now urgently calls for evidence-based explanations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".