Use and knowledge of the two FIGO systems for nongestational abnormal uterine bleeding in the reproductive years: A multinational survey
Bibliographic record
Abstract
OBJECTIVE: To evaluate the worldwide use of FIGO's two systems for the classification of causes and contributors to nongestational abnormal uterine bleeding in the reproductive years by obstetrics and gynecology professionals worldwide, to identify knowledge gaps, and explore barriers to implementation. METHODS: An electronic survey was developed by members of FIGO's Menstrual Disorders and Related Health Impacts (MDRHI) Committee to assess knowledge of abnormal uterine bleeding (AUB) and the two FIGO AUB systems among obstetricians and gynecologists. The survey was conducted online from February 28 to June 30, 2023, and comprised demographic questions, educational content inquiries, and a knowledge assessment. Available in English, Spanish, French, and Mandarin, the survey was disseminated through representatives of the World Association of Trainees in Obstetrics and Gynecology (WATOG), as well as through digital platforms and trainee-focused Facebook groups. RESULTS: Out of 1317 initial participants from 65 countries, 1114 completed the survey. The highest representation was from China (42.6%), where both trainees and clinicians participated. Participation varied across FIGO regions, with Asia-Oceania contributing the most (n = 602) and North America the least (n = 62). Most participants were in hospital-based residency programs (73.9%), graduating around 2012 with 3 years of postgraduate medical education. Nearly 70% reported being familiar with FIGO systems, while over 93% were familiar with PALM-COEIN. About one-third reported frequent use of FIGO systems by faculty, except among French-speaking respondents. Higher composite FIGO systems knowledge scores correlated with familiarity with FIGO AUB systems and PALM COEIN. Language did not significantly affect scores. CONCLUSION: FIGO's systems for nongestational AUB are widely used but gaps persist. Targeted strategies focusing on faculty development and research are needed to improve awareness and proficiency. This study highlights the necessity for interventions in medical education to enhance trainees' understanding and utilization of standardized nomenclature.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".