The two FIGO systems for normal and abnormal uterine bleeding symptoms and classification of causes of abnormal uterine bleeding in the reproductive years: 2018 revisions
Bibliographic record
Abstract
BACKGROUND: The International Federation of Gynecology and Obstetrics (FIGO) systems for nomenclature of symptoms of normal and abnormal uterine bleeding (AUB) in the reproductive years (FIGO AUB System 1) and for classification of causes of AUB (FIGO AUB System 2; PALM-COEIN) were first published together in 2011. The purpose was to harmonize the definitions of normal and abnormal bleeding symptoms and to classify and subclassify underlying potential causes of AUB in the reproductive years to facilitate research, education, and clinical care. The systems were designed to be flexible and to be periodically reviewed and modified as appropriate. OBJECTIVES: To review, clarify, and, where appropriate, revise the previously published systems. METHODOLOGY AND OUTCOME: To a large extent, the process has been an iterative one involving the FIGO Menstrual Disorders Committee, as well as a number of invited contributions from epidemiologists, gynecologists, and other experts in the field from around the world between 2012 and 2017. Face-to-face meetings have been held in Rome, Vancouver, and Singapore, and have been augmented by a number of teleconferences and other communications designed to evaluate various aspects of the systems. Where substantial change was considered, anonymous voting, in some instances using a modified RAND Delphi technique, was utilized.
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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 | 0.035 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".