A Randomized Pilot Study of a Cognitive Intervention for the Elderly with Mild Cognitive Impairment
Bibliographic record
Abstract
The extension of life increased the prevalence of many non-communicable and chronic diseases, including neurodegenerative diseases. Identifying Mild Cognitive Impairment (MCI) in the early stage would allow to providing intervention at early that may reduce the progression of Alzheimer’s and other related cognitive disorders. The present study aimed to examine the effects of a new Indian adapted Motivationally Enhanced Compensatory Cognitive Training program for the elderly with MCI (IAME-CCT-MCI). This is a 4-week intervention program, utilized randomized pilot study with single-group pretest-posttest design conducted with 32 elderly participants screened with MCI using Tamil version of Montreal Cognitive Assessment (T-MoCA). The primary outcome measure was T-MoCA used to measure cognitive impairment, followed by PGI Battery for Assessment of Mental Efficiency in the Elderly (PGI-BAMEE). PGI Memory Scale (PGI-MS) was used to measure as the secondary outcome. The PGI-BAMEE consists of four subtests to assess mental efficiency, general information, orientation towards time and place, perceptuo-motor functions, and depressive symptomology. Whereas, the PGI-MS contains a detailed assessment of ten types of memory, viz., remote memory, recent memory, attention-concentration, delayed recall, immediate recall, verbal retention for similar pairs, verbal retention for dissimilar pairs, visual retention, and recognition. Through the paired t-test analysis, the IAME-CCT-MCI intervention program significantly improved the T-MoCA score and PGI-Memory Scale scores. The subtests of PGI-BAMEE were also significantly improved during the immediate pretest-posttest period. The findings provides preliminary support for the potential efficacy of the IAME-CCT-MCI cognitive intervention when delivered to the elderly with MCI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".