CLINICAL CORRELATION OF MOCA AND TAU IMAGING
Bibliographic record
Abstract
Abstract Background Tau-PET Scan measures brain tau deposition. Montreal Cognitive Assessment (MOCA) is used for cognitive impairment in (MCI) and Alzheimer Disease (AD). We analyzed the correlation of MOCA to tau-PET imaging in a memory clinic. Hypothesis Tau-PET positivity correlates with lower MOCA scores in memory clinic patients Objectives To assess the correlation of tau-PET positivity to MOCA test scores in memory clinic patients To evaluate an MOCA score cut-off that translates optimally to tau-PET positivity Method Retrospective observational chart review study of 34 patients attending a memory clinic in Boston, MA, with MOCA score and tau-PET scan included for analysis No stratification based on initial or final diagnosis, severity of cognitive impairment, or tracer used. Baseline MOCA and a binary PET scan result were analyzed with covariates age, sex and race. Analysis done on ‘R’ v. 2.4.0. Pearson correlation estimated MOCA score to tau-PET status. Kruskal Wallis/Chi square tests for relationship of age, sex, gender and education with MOCA score and tau-PET status. ROC analyses and sensitivity/specificity were obtained. Results Tau PET positivity did not correlate significantly with MOCA score (r= -0.24, 95%CI –0.54 to -0.12, p=0.19). A cut point of 22 on the MOCA estimated tau deposition in the brain by PET scan, with an AUC of 80%, 95% CI –0.6 to –0.99, possibly indicating a ceiling effect on the MOCA. Conclusion Tau PET does not correlate with MOCA scores, possibly due to a ceiling effect in the mild impairment subjects.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".