A novel method for quantifying affective sensitivity to endogenous ovarian hormones
Bibliographic record
Abstract
Increased sensitivity to ovarian hormone changes is implicated in the etiology of reproductive mood disorders across the female lifespan, including menstrually-related mood disorders, perinatal mood disorders, and perimenopausal depression. Developing a method to accurately quantify sensitivity to endogenous hormone fluctuations may therefore facilitate the prediction and prevention of these mental health conditions. Here, we propose one such method applying a synchrony analysis to compute time-lagged cross-correlations between repeated assessments of endogenous hormone levels and self-reported affect. We apply this method to a dataset containing frequent repeated assessments of affective symptoms and the urinary metabolites of estradiol (E2) and progesterone (P4) in 94 perimenopausal females. These preliminary findings suggest that, with further refinement and validation, the proposed method holds promise as a diagnostic tool to be used in clinical practice and to advance research investigating the etiology of reproductive mood disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".