IVF add‐ons in Australia and New Zealand: A systematic assessment of IVF clinic websites
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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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 | 0.020 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".