A study of handwriting sample in geriatric population with cognitive impairment
Bibliographic record
Abstract
Objectives: Cognitive impairment in the geriatric population often remains undiagnosed until progressed enough to cause interruptions in activities of daily living. Routine tests are time taking, requiring a specialist. Handwriting function reflects the brain's cognitive capacity by involving it's both halves. It is easy to collect and does not strain the participant, and can aid in the faster diagnosis of cognitive impairment. Materials and Methods: To study handwriting parameters collected with Livescribe Echo Smart Pen and compare them with cognitive scores of Montreal Cognitive Assessment-Hindi (MOCA-H) and Addenbrooke's Cognitive Examination-Hindi (ACE-H) in a cross-sectional observational study. Handwritten parameters differentiating both cognitive groups were identified and analyzed. Results: The mean age of the study population was 66.4 (5.3) years. The mean MOCA score in the cognitively impaired (CI) and noncognitively (NCI) group was 22.67 and 27.00, respectively. The mean ACE-H score in CI and NCI group was 80.68 and 93.05, respectively. In all handwriting tasks (T1-T3), higher scores were obtained on all parameters in the CI group except text width (TW), stroke frequency (SF), and writing speed (WS). In handwriting task 3 (single letter repetition), WC (word count) was found to be higher in the NCI group. Handwriting parameters of the whole task (TOT, PSPS, TW, TH, NOL, and WS) and text line (MTOL and MTOSS) were found to be helpful in group differentiation in all three tasks. There was a moderate degree of positive correlation with handwriting parameters (PSPS, WS, and WC) and a negative correlation with handwriting parameters (NOPS, TOT, TH, NOL, MHOL, MTOL, and MTOSS) across the tasks with MOCA and ACE scores. Conclusion: Inclusion of quantitative handwriting analysis in neuropsychological assessment can be one step forward towards a simple, reliable, and faster diagnosis of geriatric cognitive impairment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".