Top 10 priorities for future ectopic pregnancy research: an international consensus development study
Bibliographic record
Abstract
OBJECTIVE: To determine the future priorities for ectopic pregnancy research. DESIGN: 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 future ectopic pregnancy research. SUBJECTS: Healthcare professionals, people with lived experience of ectopic pregnancy, and others were brought together in an open and transparent process using formal consensus methods advocated by the James Lind Alliance. EXPOSURE: Not applicable. MAIN OUTCOME MEASURES: Top 10 research priorities for ectopic pregnancy. RESULTS: The initial survey was completed by 855 participants from 35 countries, and 1,220 potential research questions were submitted. Three clinical practice guidelines and 43 Cochrane systematic reviews identified a further 24 potential research questions. A rationalized list of 49 confirmed research uncertainties was entered into an interim prioritization survey completed by 413 respondents from 20 countries. The top 10 research priorities were identified during a consensus development meeting involving 37 participants from 10 countries. These research priorities are diverse and seek answers to questions regarding prevention, treatment, and the longer-term impact of ectopic pregnancy. CONCLUSION: We anticipate that the identified research priorities, developed to specifically highlight the most pressing clinical needs as perceived by healthcare professionals, people with lived experience of ectopic pregnancy, and others, will help research funding organizations and researchers to develop their future research agenda.
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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 | 0.099 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".