INTEGRATIVE ANALYSIS OF LONGITUDINAL STUDIES ON AGING AND DEMENTIA (IALSA)
Bibliographic record
Abstract
Cross-validation of research findings across independent longitudinal studies is essential for building the most effective evidence base for successful cumulative science in gerontology. In many cases, cross-study differences in measurements and sample composition (e.g., ability level, education, language) impede the utility of pooled data analysis, particularly in the case of longitudinal studies. Harmonization can occur at the levels of research question, statistical models, and measurements, permitting synthesis of results for understanding ways in which birth cohort, country, culture, and issues of mortality and selection relate to outcomes and differences across studies. The goal of the Integrative Analysis of Longitudinal Studies of Aging and Dementia (IALSA: NIH/NIA P01AG043362) research network encompassing over 100 studies from around the world is to maximize opportunities for international reproducible research and cross-validation across heterogeneous sources of evidence by evaluating comparable statistical models, with comparison of the pattern and magnitudes of effects at the construct level. This symposia describes network activities and methods and provides multiple examples for rigorous cross-study comparison based on the coordinated analysis approach.
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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 | 0.222 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".