Additional file 1 of Developing a gender measure and examining its association with cardiovascular diseases incidence: a 28-year prospective cohort study
Bibliographic record
Abstract
Additional File 1: Tables S1-S11. Table S1- Participant characteristics according the to sex, at baseline. Table S2- Associations between female sex at baseline and the 28-year CVD incidence. Table S3- Associations between gender tertiles at baseline and the 28-year CVD incidence after excluding psychosocial stressors at work and working hours, stratified by sex. Table S4- Associations between gender tertiles at baseline and the 28-year CVD incidence after excluding personality traits, stratified by sex. Table S5- Associations between gender-related variables at baseline and the 28-year CVD incidence, stratified by sex. Table S6- Associations between gender score across sextiles at baseline and the 28-year CVD incidence, stratified by sex. Table S7- Associations between gender tertiles at baseline and the 28-year CVD incidence, including all 16 gender-related variables, stratified by sex. Table S8- Frequency of the 16 gender-related variables for males and females. Table S9- Frequency of the 16 gender-related variables for males and females. Table S10- Frequency of the 16 gender-related variables for males and females. Table S11- Associations between the gender-related variables at the 8-year follow-up and sex, modelling the probability of being a female.
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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.659 | 0.059 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".