IVF add‐ons in Australia and New Zealand: A systematic assessment of IVF clinic websites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In vitro fertilisation (IVF) 'add-ons' are extra (non-essential) procedures, techniques or medicines, which usually claim to increase the chance of a successful IVF outcome. Use of IVF add-ons is believed to be widespread in many settings; however, information about add-on availability in Australasia is lacking. AIMS: To understand which add-ons are advertised on Australasian IVF clinic websites, and what is the evidence for their benefit. MATERIALS AND METHODS: A systematic assessment of website content was undertaken between December 2019-April 2020, capturing IVF add-ons advertised, including costs, claims of benefit, statements of risk or limitations, and evidence of effectiveness for improving live birth and pregnancy. A literature review assessed the strength and quality of evidence for each add-on. RESULTS: Of the 40 included IVF clinics websites, 31 (78%) listed one or more IVF add-ons. A total of 21 different add-ons or add-on groups were identified, the most common being preimplantation genetic testing for aneuploidies (offered by 63% of clinics), time-lapse systems (33%) and assisted hatching (28%). In most cases (77%), descriptions of the IVF add-ons were accompanied by claims of benefit. Most claims (90%) were not quantified and very few referenced scientific publications to support the claims (9.8%). None of the add-ons were supported by high-quality evidence of benefit for pregnancy or live birth rates. The cost of IVF add-ons varied from $0 to $3700 (AUD/NZD). CONCLUSIONS: There is widespread advertising of add-ons on IVF clinic websites, which report benefits for add-ons that are not supported by high-quality evidence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it