[P2–308]: VERIFYING STABILITY OF NEUROPSYCHOLOGICAL TOOLS THROUGH LONGITUDINAL FOLLOW‐UP OF PATIENTS WITH COGNITIVE DISORDER
Bibliographic record
Abstract
Neuropsychological tools such as MMSE, MOCA, ADL, clock drawing test (CDT), block design (BD), trail making test (TMT), Benton et al are important for the diagnosis and treatment follow up of cognitive disorder patients. We hope they have nice stability and precisely reflect disease progression and treatment effect. 68 patients with cognitive disorder (AD, MCI, FTLD, LBD, SCD, CAA et al) and 114 follow-ups (1–5 per patient) were included. For tools that lower score meant disease progression such as MMSE, increase score on follow up than last visit (rule 1) or than average of all the visits before (rule 2) was defined as one abnormal point, and for ADL which would increase score with disease progression, the opposite. Finally, we calculated the abnormal rates of all the tools and the sub-domains. Lower abnormal rates meant higher stability of the tools. Also, we analyzed the effect of anxiety and depression to abnormal scores of the tools. For rule 1, abnormal rate was MMSE 27.2%, MOCA 40.6%, ADL 27.4%, Benton 14.8%, BD 7.1%, CDT 15.9%, TMT 12.0%. On sub-domains of MMSE, naming (3.5%) and reading (2.7%) were most stable. On sub-domain of MOCA, naming (6.5%) and calculating (100–7) (3.2%) were most stable. For ADL, ability such as teeth brushing, walking, sitting down and standing up, toileting could most stably reflect patients’ disease progression. Stability of Memory, Verbal fluency test and digit span test was easily affected by anxiety and depression. For rule 2, abnormal rate was MMSE 29.8%, MOCA 45.3%, ADL 33.3%, Benton 18.5%, BD 7.1%, CDT 17.5%, TMT 16.0%. Sub-domain results were same as rule 1. MMSE was a relatively stable tool for evaluating disease progression and treatment effect of dementia patients, while ADL also useful. Specified sub-domains of these scales could stably reflect disease progression and be the best choice in clinical practice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".