Bibliographic record
Abstract
Abstract Neurocognitive aging research has predominantly adopted a linear approach, categorizing aging populations homogeneously. Researchers like Salthouse argue that this perspective perceives cognitive decline as almost linear from early adulthood. However, emerging findings challenge this view, highlighting significant variability in cognitive aging and proposing different subtyping methods to study the neurocognitive aging population. Our talk will propose a novel subtyping framework inspired by the Orchid and Dandelion theory from Boyce (2005). This new framework introduces a nuanced understanding of cognitive aging, categorizing individuals based on genetic and phenotypic characteristics to better capture the heterogeneity of cognitive abilities as people age. Using extensive datasets such as the UK Biobank and machine learning analytical techniques, our research investigates the impact of environmental factors on distinct cognitive aging subtypes. This approach diverges from the traditional linear models, revealing that lifestyle factors have varying impacts on different cognitive aging subtypes, underscoring the theory’s premise that some individuals (orchids) are more environmentally sensitive, affecting their cognitive aging process. In contrast, others (dandelions) show resilience. This paradigm shift towards recognizing cognitive aging’s inherent heterogeneity offers a more accurate and personalized understanding of aging, emphasizing the importance of subtyping in developing targeted interventions. Our findings advocate for a move beyond the “linear” methodology found in the literature, highlighting the need for a framework that accommodates the heterogeneity of cognitive aging.
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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".