An assessment of neurocognitive speed in relation to frailty
Bibliographic record
Abstract
OBJECTIVES: to evaluate the relationship between neurocognitive speed (NCS) and frailty; to consider how this relationship is affected by how frailty is operationalised. DESIGN: secondary analysis of the baseline cohort of the Oxford Project To Investigate Memory and Aging (OPTIMA), a longitudinal observational cohort. SUBJECTS: of 388 participants who underwent a comprehensive intake assessment followed by an annual follow-up for at least 3 years, data on all measures were available on 164 people. MEASUREMENTS: NCS was defined as a combined score of <18 on the pattern comparison test (<11 is abnormal) and letter comparison test (<7 is abnormal). Frailty was defined from a modified Phenotype model, the Edmonton Frailty Scales (EFS) and a frailty index (FI); the latter two were adapted here to exclude cognitive measures. RESULTS: in multivariate logistic (NCS as < or ≥18) and linear regression (NCS as continuous variable), only the FI (OR = 0.87) was significant (P < 0.05). When all frailty measures were included in the multivariate analysis only, FI (OR = 0.88) was significant (P < 0.05). Mini-mental Status Examination remained significantly related to NCS throughout all analysis. CONCLUSION: NCS slows with increasing frailty as shown with the FI.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".