Top 10 priorities for future infertility research: an international consensus development study
Bibliographic record
Abstract
Study Question Can the priorities for future research in infertility be identified? Summary Answer The top 10 research priorities for the four areas of male infertility, female and unexplained infertility, medically assisted reproduction, and ethics, access, and organization of care for people with fertility problems were identified. What is Known Already Many fundamental questions regarding the prevention, management, and consequences of infertility remain unanswered. This is a barrier to improving the care received by those people with fertility problems. Study Design, Size, Duration Potential research questions were collated from an initial international survey, a systematic review of clinical practice guidelines, and Cochrane systematic reviews. A rationalized list of confirmed research uncertainties was prioritized in an interim international survey. Prioritized research uncertainties were discussed during a consensus development meeting. Using a formal consensus development method, the modified nominal group technique, diverse stakeholders identified the top 10 research priorities for each of the categories male infertility, female and unexplained infertility, medically assisted reproduction, and ethics, access, and organization of care. Participants/Materials, Setting, Methods Healthcare professionals, people with fertility problems, and others (healthcare funders, healthcare providers, healthcare regulators, research funding bodies and researchers) were brought together in an open and transparent process using formal consensus methods advocated by the James Lind Alliance. Main Results and the Role of Chance The initial survey was completed by 388 participants from 40 countries, and 423 potential research questions were submitted. Fourteen clinical practice guidelines and 162 Cochrane systematic reviews identified a further 236 potential research questions. A rationalized list of 231 confirmed research uncertainties were entered into an interim prioritization survey completed by 317 respondents from 43 countries. The top 10 research priorities for each of the four categories male infertility, female and unexplained infertility (including age-related infertility, ovarian cysts, uterine cavity abnormalities, and tubal factor infertility), medically assisted reproduction (including ovarian stimulation, IUI, and IVF), and ethics, access, and organization of care, were identified during a consensus development meeting involving 41 participants from 11 countries . These research priorities were diverse and seek answers to questions regarding prevention, treatment, and the longer-term impact of infertility. They highlight the importance of pursuing research which has often been overlooked, including addressing the emotional and psychological impact of infertility, improving access to fertility treatment, particularly in lower resource settings, and securing appropriate regulation. Addressing these priorities will require diverse research methodologies, including laboratory-based science, qualitative and quantitative research, and population science. Limitations, Reasons for Caution We used consensus development methods, which have inherent limitations, including the representativeness of the participant sample, methodological decisions informed by professional judgement, and arbitrary consensus definitions. Wider Implications of the Findings We anticipate that identified research priorities, developed to specifically highlight the most pressing clinical needs as perceived by healthcare professionals, people with fertility problems, and others, will help research funding organizations and researchers to develop their future research agenda. Study Funding/ Competing Interest(s) The study was funded by the Auckland Medical Research Foundation, Catalyst Fund, Royal Society of New Zealand, and Maurice and Phyllis Paykel Trust. Geoffrey Adamson reports research sponsorship from Abbott, personal fees from Abbott and LabCorp, a financial interest in Advanced Reproductive Care, committee membership of the FIGO Committee on Reproductive Medicine, International Committee for Monitoring Assisted Reproductive Technologies, International Federation of Fertility Societies, and World Endometriosis Research Foundation, and research sponsorship of the International Committee for Monitoring Assisted Reproductive Technologies from Abbott and Ferring. Siladitya Bhattacharya reports being the Editor-in-Chief of Human Reproduction Open and editor for the Cochrane Gynaecology and Fertility Group. Hans Evers reports being the Editor Emeritus of Human Reproduction. Andrew Horne reports research sponsorship from the Chief Scientist's Office, Ferring, Medical Research Council, National Institute for Health Research, and Wellbeing of Women and consultancy fees from Abbvie, Ferring, Nordic Pharma, and Roche Diagnostics. M. Louise Hull reports grants from Merck, grants from Myovant, grants from Bayer, outside the submitted work and ownership in Embrace Fertility, a private fertility company. Neil Johnson reports research sponsorship from Abb-Vie and Myovant Sciences and consultancy fees from Guerbet, Myovant Sciences, Roche Diagnostics, and Vifor Pharma. José Knijnenburg reports research sponsorship from Ferring and Theramex. Richard Legro reports consultancy fees from Abbvie, Bayer, Ferring, Fractyl, Insud Pharma and Kindex and research sponsorship from Guerbet and Hass Avocado Board. Ben Mol reports consultancy fees from Guerbet, iGenomix, Merck, Merck KGaA and ObsEva. Ernest Ng reports research sponsorship from Merck. Craig Niederberger reports being the Co Editor-in-Chief of Fertility and Sterility and Section Editor of the Journal of Urology, research sponsorship from Ferring, and retains a financial interest in NexHand. Jane Stewart reports being employed by a National Health Service fertility clinic, consultancy fees from Merck for educational events, sponsorship to attend a fertility conference from Ferring, and being a clinical subeditor of Human Fertility. Annika Strandell reports consultancy fees from Guerbet. Jack Wilkinson reports being a statistical editor for the Cochrane Gynaecology and Fertility Group. Andy Vail reports that he is a Statistical Editor of the Cochrane Gynaecology & Fertility Review Group and of the journal Reproduction. His employing institution has received payment from HFEA for his advice on review of research evidence to inform their ‘traffic light' system for infertility treatment ‘add-ons'. Lan Vuong reports consultancy and conference fees from Ferring, Merck and Merck Sharp and Dohme. The remaining authors declare no competing interests in relation to the present work. All authors have completed the disclosure form. Trial Registration Number Not applicable.
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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 | 0.426 | 0.397 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".