Feasibility of computerized working memory training in individuals with Huntington disease
Bibliographic record
Abstract
OBJECTIVES: Huntington disease (HD) is associated with a variety of cognitive deficits, with prominent difficulties in working memory (WM). WM deficits are notably compromised in early-onset and prodromal HD patients. This study aimed to determine the feasibility of a computerized WM training program (Cogmed QM), novel to the HD population. METHODS: Nine patients, aged 26-62, with early stage HD underwent a 25-session (5 days/week for 5 weeks) WM training program (Cogmed QM). Training exercises involved the manipulation and storage of verbal and visuospatial information, with difficulty adapted as a function of individual performance. Neuropsychological testing was conducted before and after training, and performance on criterion WM measures (Digit Span and Spatial Span), near-transfer WM measures (Symbol Span and Auditory WM), and control measures were evaluated. Post-training interviews about patient experience were thematically analyzed using NVivo software. RESULTS: Seven of nine patients demonstrated adherence to the training and completed all sessions within the recommended timeframe of 5 weeks. All adherent patients showed improvement on the Cogmed tasks as defined by the Improvement Index (M = 22.17, SD = 8.84, range = 13-36). All adherent patients reported that they found training helpful (n = 7), and almost all felt that their memory improved (n = 6). Participants also expressed that the training was difficult, sometimes frustrating, and time consuming. CONCLUSIONS: This pilot study provides support for feasibility of computerized WM training in early-stage patients with HD. Results suggest that HD patients perceive benefits of intensive WM training, though a full-scale and controlled intervention project is needed to understand the size of the effect and reliability of changes over time. TRIAL REGISTRATION: ClinicalTrials.gov, Registry number NCT02926820.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".