Gender score development in a retrospective approach in the Berlin Aging Study II
Bibliographic record
Abstract
Abstract In addition to biological sex, gender, the sociocultural dimension of being a woman or a man, plays a central role in health. However, there are so far no approaches to quantify gender in a retrospective manner in existing study datasets. We therefore aimed to develop a methodology that can be retrospectively applied to assess gender in existing cohorts. We used baseline data from the Berlin Aging Study II (BASE-II), obtained in 2009-2014 from 1869 participants aged 60 years and older. We identified 13 gender related variables and used them to construct a gender score (GS) by primary component and logistic regression analysis. Of these, 9 variables contributed to a gender score: chronic stress, marital status, risk taking behavior, agreeableness, neuroticism, extraversion, loneliness, conscientiousness, and education. GS differed significantly between females and males as defined by sex. Next, we calculated linear regressions to investigate associations between sex, GS, and selected biological and well-being variables. Sex, but not GS was significantly associated with LDL-C and TC. GS, but not sex, was significantly associated with cortisol levels, CES-depression, negative affect, life satisfaction. Thus, we were able to develop a GS in a retrospective manner from available study variables that characterized women and men in addition to biological sex. This approach will allow us to introduce the notion of gender retrospectively into a large number of studies.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".