2018 Canadian Consortium on Neurodegeneration in Aging (CCNA) Science Day Abstracts (Montréal, Québec - October 1-2, 2018)
Bibliographic record
Abstract
Background: Age-specific risk and incidence of dementia appears to be decreasing internationally.A current hypothesis for this decrease is that societal investments over time have led to improvements in population health with resultant effects on cognition and brain reserve.To assess if brain reserve has indeed been increasing amongst individuals born in later decades of the 20th century, we examined whole brain volume as an indicator of global neurodegeneration from the 1920s to the 1950s.Methods: Participants (at baseline visit: n=1,580, age=73.84±7.23 years, %female=43.8%)from three diagnostic groups (cognitively normal individuals [CN] (n=407), Mild Cognitive Impairment [MCI] (n=852), and Alzheimer's disease [AD] (n=321)) were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI).The relationship between whole brain volume (expressed as percentage of intracranial volume) and decade of birth was assessed with a linear regression model in R (version 3.4.3)accounting for age, sex, years of education, Apolipoprotein-ε4 alleles, and Mini-Mental State Exam.Results: Participants born in later decades had significantly greater percentage whole brain volumes in all three diagnostic groups [CN: β=3.96 (95% CI: 3.00-4.93),p< 0.001, adj.R 2 =0.39;MCI: β=4.16 (3.38-4.94),p<.001, adj.R 2 =.37; AD: β=4.66 (3.57-5.74),p<.001, adj.R 2 =.26)].Adjusted R 2 values were low suggesting low model predictive ability.Conclusions: Participants born in later decades show a trend towards higher brain reserve as indexed by brain volume.This suggests a possible contribution to decline in age-specific incidence of dementia.We plan to examine this finding further in the COMPASS-ND cohort and improve data exploration and modeling.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.258 | 0.054 |
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".