Bibliographic record
Abstract
Abstract With more than1.35 billion people, India, the second-most populous country in the world, is soon to experience rapid aging of its population. By2050, India’s older population is projected to reach320 million (about the current size of the entire U.S. population). In this session we introduce the Longitudinal Aging Study in India – Diagnostic Assessment of Dementia (LASI-DAD), a new cohort study designed to advance dementia research to better understand late-life cognition, cognitive aging, cognitive impairment, and dementia, as well as their risk and protective factors. LASI is a prospective, multi-purpose population survey of older adults aged45 and older, representative of the entire country and of each state (N~72,000). LASI-DAD is an in-depth study of late-life cognition and dementia, drawing a sub-sample of older adults aged60 and older from LASI (N~4,300). It administered the Harmonized Cognitive Assessment Protocol (HCAP), which consists of a pair of in-person interviews, one with the target respondent and one with an informant nominated by the respondent. The respondent interview includes a neuropsychological test battery designed to measure a range of key cognitive domains affected by cognitive aging and Alzheimer’s Diseases. We organize the session to showcase LASI-DAD. Specifically, the session consists of four papers, including: (1) the introduction of the design and methodology, (2) the latent structure of neuropsychological test results, (3) the investigation of the relationship between visual impairment and cognition, and (4) the examination of female disadvantage in dementia and its association with cross-state variations in gender inequality.
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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".