M15 Computerised cognitive training for individuals with early stage huntington’s disease
Bibliographic record
Abstract
Background Huntington’s disease (HD) is associated with a variety of cognitive deficits, with prominent deficits in working memory (WM). These deficits can occur in the early stage of the disease and undermine quality of life. Currently, there are no established treatments for these symptoms. Aims The feasibility of implementing a home-based, computerised WM training program was examined in HD patients who reported WM difficulties in daily life. A secondary aim was to assess the patient experience with this training program. 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, to evaluate changes in performance on criterion WM measures (Digit Span and Spatial Span) and near-transfer WM measures (Symbol Span and Auditory WM). Post-training interviews about patient experience were thematically analysed using NVivo software. Results Seven of nine (77%) patients completed the program within the recommended timeframe (M = 36 ± 12.35 days). Compared to baseline scores, patients showed significant improvement on both measures of verbal WM (Digit Span, p = 0.047; Auditory WM, p = 0.041). Each of these patients reported that they found training helpful, and almost all (n = 6) felt that their memory improved. Conclusions This pilot study provides support for feasibility of computerised WM training in early-stage patients with HD. Results suggest that HD patients can improve WM with intensive training, though a full-scale intervention project is needed to understand the reliability of changes over time.
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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 | 0.000 |
| 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) | 0.003 | 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".