The impact of Covid‐19 on gamete shipping by Australian and New Zealand patients
Bibliographic record
Abstract
BACKGROUND: Cross-border surrogacy and egg donor arrangements are an increasingly common means to family building. Establishing patterns of use has always been difficult in relation to Australian patients. Accurate data is stymied by lack of documentation of international third-party reproductive care available to Australian authorities. When international travel bans came into effect, it is hypothesised that those planning to use cross-border reproductive care had to rely significantly more on local in vitro fertilisation (IVF) clinics for services such as sperm freezing, embryo creation and gamete release procedures. AIM: To quantify and characterise the impact of the Covid-19-related travel ban on international and interstate gamete shipping by Australian IVF clinics. MATERIALS AND METHODS: Thirty-one Australian and New Zealand IVF clinics were invited to provide de-identified data on interstate and international gamete export applications from two 12 month time periods pre- and during Covid-19-related international travel lockdowns. Seven IVF organisations provided data on: patient age; type of gametes exported; destination country/state; and date gamete release approved. RESULTS: Most gametes (78%) were shipped to another Australian IVF clinic and 22% internationally. Patient-initiated shipping domestically and internationally both showed significant increases when comparing pre- and post-Covid datasets. Of the 21 destination countries reported for international shipments, the US was the commonest (39%), followed by Ukraine (21%) and Canada (9%). CONCLUSIONS: The inability of involuntarily infertile patients to travel internationally, rather than halt cross-border reproductive care, has led to a significant increase in the uptake of gamete shipping. The high proportion of internationally shipped gametes going to the US and Ukraine is likely a reflection of the availability of surrogates and donors and more amenable legal frameworks.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".