Premature ovarian insufficiency: an International Menopause Society White Paper
Bibliographic record
Abstract
The aim of this International Menopause Society White Paper on premature ovarian insufficiency (POI) is to provide the latest information regarding this distressing condition. The impact of POI has far-reaching consequences due to its impact on general, psychological, and sexual quality of life, fertility prospects, and long-term bone, cardiovascular, and cognitive health. Progress in fully understanding the etiology, diagnosis, and optimal management options has been slow thus far due to the complexity of the condition and fragmented research. Recent advances in epidemiological and genetic research have improved our understanding of this condition and randomized prospective trials are being planned to determine the intervention strategies, which will optimize quality of life and long-term well-being. The International Menopause Society has commissioned a number of experts at the forefront of their specialty to define the state of the art in the understanding of this condition, to advise on practical management strategies, and to propose future research strategies. It is hoped that a global task force will subsequently be convened in order to formulate a consensus statement across key societies, to accelerate date collection and analysis of a global POI registry, and to facilitate progress in the key defined areas of research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".