Additional file 1 of Serum ferritin and incident cardiometabolic diseases in Scottish adults
Bibliographic record
Abstract
Additional file 1: Figure S1. Identification of the cohort SHeS 95-98. Among the individuals removed from the sample for having no linkage to Scottish Morbidity Record (SMR): 14 (0.3%) had dead status, 323 (7.8%) migrated outwith Scotland, 1603 (38.6%) did not consent to data linkage, 264 (6.4%) had not linkage to Community Health Index (CHI) , and 1952 (47%) had CHI but had not reason described for not linkage to SMR. Figure S2. Adjusted hazard ratios for CEVD by sex/menopausal specific Z score of ferritin levels. Table S1. HRs and 95% CI for the incidence of diabetes and cardiovascular diseases by serum ferritin quintiles with middle quintile as reference. Table S2. HRs and 95% CI* for the incidence of diabetes and cardiovascular diseases by serum ferritin levels (weighted analysis). Table S3. Final multivariate models for each cardiometabolic disease. Table S4. Use of medicines and vitamin/dietary supplements at baseline by sex-and menopausal status-specific quartiles of ferritin level in the study cohort. Table S5. Types of comorbidities in by ferritin levels in the individuals of the study
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.733 | 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; 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".