Structural brain markers are differentially associated with neurocognitive profiles in socially marginalized people with multimorbid illness.
Bibliographic record
Abstract
OBJECTIVE: The authors examined associations between complementary fronto-temporal structural brain measures (gyrification, cortical thickness) and neurocognitive profiles in a multimorbid, socially marginalized sample. METHOD: Participants were recruited from single-room occupancy hotels and a downtown community courthouse (N = 299) and grouped on multiple neurocognitive domains using cluster analysis. Subsequently, the authors evaluated whether the fronto-temporal brain indices, and proxy measures of neurodevelopment and acquired brain insult/risk exposure differentiated members of the 3 distinct neurocognitive clusters. RESULTS: Greater frontal and temporal gyrification and more proxies of aberrant neurodevelopment were associated with the lowest functioning neurocognitive cluster (Cluster 3). Further, for older participants (50+ years), increased cortical thickness in frontal regions was associated with the higher functioning neurocognitive cluster (Cluster 1). Finally, the greatest acquired brain insult/risk exposure was associated with the cluster characterized by selective decision-making impairment (Cluster 2). CONCLUSIONS: Fronto-temporal structural brain indices, and proxies of neurodevelopment and acquired brain insult/risk exposure were differentially associated with neurocognitive profiles in socially marginalized persons. These findings highlight the unique pathways to neurocognitive impairment in a heterogeneous population and help to clarify the vulnerabilities confronted by different subgroups. (PsycINFO Database Record
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".