<scp>FIGO</scp> guideline on liver disease and pregnancy
Bibliographic record
Abstract
The number of women entering pregnancy with chronic liver disease is rising. Gestational liver disorders affect 3% of the pregnant population. Both can be associated with significant maternal and fetal morbidity and mortality. European guidance has recently been published to inform management. This FIGO (the International Federation of Gynecology & Obstetrics) guideline aims to use the latest evidence to inform practice relevant to a global population. The immediate past and present chairs of FIGO's Committee on the Impact of Pregnancy on Long-term Health invited the Chair of the European guideline, alongside two trainees with an interest in liver disorders in pregnancy, to develop a guideline relevant to a global audience, thus serving the real-world population and fulfilling FIGO's ambition to enhance their global voice for women's health. Experts in the field with experience in managing liver disorders in pregnancy from a diverse selection of continents helped to develop a guideline. The guideline includes the most common pre-existing and gestational liver disorders. Evidence-based best practice recommendations are summarized in addition to pragmatic recommendations. Printable tables/figures are included in the guideline for ease of use. These include a table of normal ranges of commonly used blood tests, a table outlining safety of investigations, and a table of delivery considerations relevant to a global audience. Figures designed to summarize each section of the guideline and the multidisciplinary approach to managing liver disorders in pregnancy are also included. This guideline incorporates guidance for a global audience aimed at improving the management of women with pre-existing and new liver disease in pregnancy.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.097 | 0.056 |
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".