What do women undergoing <i>in vitro</i> fertilization (IVF) understand about their chance of IVF success?
Bibliographic record
Abstract
STUDY QUESTION: How well informed are Australian women who undergo IVF about their chances of having a baby? SUMMARY ANSWER: Only one in four women estimated their individual chance of success with IVF accurately, with most women overestimating their chance. WHAT IS KNOWN ALREADY: Limited knowledge about infertility and infertility treatment in the general population is well-documented. The few studies that have investigated patients' knowledge about the chance of IVF success suggest that while IVF patients are aware of average success rates, they tend to be unrealistic about their own chance of success. STUDY DESIGN, SIZE, DURATION: We conducted an anonymous online survey of 217 women who had started IVF since 2018 in Australia. The survey was advertised on social media, enabling women from across Australia to participate. Responses were collected in June 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS: The survey included questions on demographic characteristics and IVF history. It asked what participants thought their chance of having a baby from one IVF treatment cycle was, how they rated their knowledge about chance of success, and about their experience of receiving IVF-related information. Participants' estimations of their chance of success were compared with their chance as calculated by the Society for Assisted Reproductive Technology's (SART) online calculator. Responses to a free-text question about what information women wished they had been given when they started treatment were analysed thematically. MAIN RESULTS AND THE ROLE OF CHANCE: Only about a quarter (58/217, 27%) of participants accurately estimated their chance of having a baby within 20% relative to their SART calculated chance, with more than half (118/217, 54%) overestimating their chance. Ninety percent of women indicated that their preferred source of treatment information was a consultation with their doctor, despite less than half (44%) reporting that doctors explained the probability of having a baby with IVF well (mean 5.9/10). In free-text responses, many women also reported that they wished they had been given more realistic information about IVF and their chance of success. LIMITATIONS, REASONS FOR CAUTION: The dissemination method precludes calculation of response rate, and it is not possible to know if participants are representative of all women undergoing IVF. Additionally, we only surveyed women undergoing IVF, while those who decided not to have IVF were not included. Therefore, women who overestimated their chance may have been overrepresented. There is also inherent imprecision in the way understanding of chance of success was estimated. The potential impact of recall bias could neither be quantified nor excluded. It is difficult to determine to what extent women's lack of understanding of what is possible with IVF is due to poor information-provision by clinicians and the clinic, and how much can be explained by optimism bias. WIDER IMPLICATIONS OF THE FINDINGS: The finding of poor understanding of personal chance of success amongst women undergoing IVF in Australia requires further investigation to determine potential reasons for this. The findings can be used by clinics to develop strategies for improvement in the information-provision process to ensure that women can make informed decisions about their fertility treatment. STUDY FUNDING/COMPETING INTEREST(S): This study received no external funding. S.L. is supported by a NHMRC Investigator Grant (APP1195189). R.W. is supported by a NHMRC Investigator Grant (APP2009767). B.W.M. is supported by a NHMRC Investigator Grant (GNT1176437). B.W.M. reports consultancy for Merck and ObsEva and has received research funding and travel funding from Merck. The other authors have no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".