IC‐P‐093: Deformation Based Morphometry Study of Retired CFL Football Players
Bibliographic record
Abstract
precursor to mild cognitive impairment (MCI) and later AD.This study examined cerebral blood flow (CBF) as a potential biomarker of changes in brain physiology in participants at risk for progression to AD based on SCD or MCI.Methods: 51 participants were included from the Indiana Alzheimer Disease Center, including 27 cognitively normal older adults (CN), 18 participants with SCD, and 7 participants with MCI.The total score from the first 12 items of the Cognitive Change Index (CCI)-Self was used to define SCD.CBF maps were obtained by processing 3D pCASL images using a 3D GRASE sequence in a 3T Siemens Prisma Scanner [1].CBF maps were smoothed and normalized.Group comparisons were performed using an ANCOVA model in SPM8, covaried for age and gender.Results: The SCD group showed elevated CBF relative to MCI in left posterior cingulate and relative to MCI and CN in left superior temporal gyrus, right parahippocampal gyrus, and right medial occipital gyrus (Figure 1).Further, MCI showed a trend for reduced CBF in these regions relative to CN.Within the SCD group, CBF in these areas showed a significant negative association with the MoCA total score.Across all groups, the left posterior cingulate CBF measure showed a positive association with delayed memory on the Rey Verbal Learning Test and negative association with the CCI-Informant total score.Conclusions: SCD participants show increased resting CBF in comparison to CN and MCI groups, which may indicate an early compensatory physiological response.The negative correlation of CBF and MoCA scores within the SCD group supports this hypothesis.However, across all groups, greater CBF appears related to better cognition.Replication and longitudinal follow-up are needed to clarify these initial findings and further evaluate CBF as a biomarker in those at risk for AD.[1] Jann, et al. (2015) NeuroImage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".