Bibliographic record
Abstract
In Brief Epidemiological and clinic data support the notion that some women may be at higher risk for developing mood and anxiety symptoms and cognitive complaints during certain periods in life that are marked by intense hormone variations and psychosocial stressors. The complexity of the so-called windows of vulnerability poses a particular challenge to professionals involved in the care of female patients. Menopausal transition is perhaps a paramount example; the process itself is marked by progressive, dynamic changes in hormone levels and reproductive function that interact with the aging process, changes in metabolism, sexuality, lifestyle behaviors, and overall health. The putative compounded burden of health challenges associated with this transition has become a main focus of attention of physicians and researchers who aim to identify preventive and/or early intervention strategies to promote healthy aging in midlife women. Recent studies have provided further evidence that the menopausal transition may be not only a window of vulnerability for depression and cognitive impairment but also a critical "window of opportunity" for the success of hormone-based treatments. The need for further investigation and better understanding of common underlying mechanisms seems intuitive. An ultimate goal could include preventive strategies for women presenting with various risk factors for cardiovascular, cognitive, and mood disorders as well as treatments that could be tailored to multiple symptom domains during the menopausal transition. Menopausal transition may not only be a "window of vulnerability" for depression and cognitive impairment but also a critical "window of opportunity" for the success of hormone-based treatments. This personal perspective challenges clinicians and researchers to pursue a more comprehensive approach and to tailor treatment strategies while managing symptomatic midlife women.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".