Additional file 1 of Weight variability and cardiovascular outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Appendix S1. Search Strategies. Appendix S2. Description of Emails. Appendix S3. List of Inclusion and Exclusion Criteria. Table S1. Descriptions and Definitions of Weight Variability Metrics. Table S2. Table of Study Characteristics. Table S3. Additional Table of Study Characteristics. Figure S1. Results of Per +1 SD in Weight Variability Analysis. Figure S2. Results of Degree of BMI Variability Analysis. Figure S3. Results of Ethnicity Stratification. Figure S4. Results of Diabetes Status Stratification. Figure S5. Results of Metric of Variability Stratification. Figure S6. Results of Quantile Stratification. Figure S7. Results of Previous Cardiovascular Disease Stratification. Figure S8. Results of adjustment for change in BMI or average BMI stratification. Figure S9. Results of Univariate Meta-Regression by Age. Figure S10. Egger’s Regression and Funnel Plots. Figure S11. Results of Newcastle-Ottawa Bias Analysis. Appendix S4. MOOSE Checklist. Table S4. Newcastle – Ottawa Scale Quality Assessment Results. Appendix S5. Newcastle-Ottawa Quality Assessment Scale.
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 | 0.005 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.823 | 0.045 |
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".