A – 116 Use of Cognitive Screeners in the Detection of Primary Progressive Aphasia (PPA)
Bibliographic record
Abstract
Abstract Objective Primary progressive aphasia (PPA) is a neurodegenerative disease characterized by gradual progression of primary language dysfunction. There is minimal research examining the use of cognitive screening measures with individuals with PPA. Method This study explored performance of PPA patients on the Mini Mental Status Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The study included a retrospective chart review of neuropsychological reports of 42 adults (age range = 54-89; M age = 72.98; 67% female; 95% Caucasian; M education = 14.83 years) diagnosed with mild cognitive impairment (MCI) or dementia due to PPA following comprehensive neuropsychological evaluation. Results Raw scores and diagnostic classification accuracy were calculated on the MMSE and MoCA. The MMSE accurately identified 56% of individuals with MCI (M=24.56; n=9) and 85% with dementia (M=17; n=20). The MMSE did not identify MCI/dementia for 3/4 agrammatic, 2/10 logopenic, and 2/11 semantic patients, using the recommended cutoffs. The MoCA accurately classified 100% of MCI (M=16.5; n=4) and dementia (M=11.89; n=9) patients. Patients administered MoCA had an average of 1.54 years since symptom onset, compared to 2.79 years with the MMSE. Conclusion Preliminary data suggests the MoCA may be more sensitive to detecting PPA and possibly able to detect PPA earlier in the disease course compared to the MMSE. Discrepancies between the language items across measures likely accounts for this. Additional investigation is warranted to determine how screening performance may align with neuropsychological testing.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".