Psychiatric Symptoms and Disorders Associated with Reproductive Cyclicity in Women: Advances in Screening Tools
Bibliographic record
Abstract
Female-specific psychiatric illness including premenstrual dysphoria, perinatal depression, and psychopathology related to the perimenopausal period are often underdiagnosed and treated. These conditions can negatively affect the quality of life for women and their families. The development of screening tools has helped guide our understanding of these conditions. There is a wide disparity in the methods, definitions, and tools used in studies relevant to female-specific psychiatric illness. As a result, there is no consensus on one tool that is most appropriate for use in a research or clinical setting. In reviewing this topic, we hope to highlight the evolution of various tools as they have built on preexisting instruments and to identify the psychometric properties and clinical applicability of available tools. It would be valuable for researchers to reach a consensus on a core set of screening instruments specific to female psychopathology to gain consistency within and between clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".