Repetition-lag memory training is feasible in patients with chronic stroke, including those with memory problems
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Repetition-lag memory training was developed to increase individuals' use of recollection as opposed to familiarity in recognition memory. The goals of this study were to examine the feasibility of repetition-lag training in patients with chronic stroke and to explore whether the training might show suggestions of transfer to non-trained tasks. RESEARCH DESIGN: Quasi-experimental. METHODS AND PROCEDURES: Patients (n = 17) took part in six repetition-lag training sessions and their gains on the training and non-trained tasks were compared to those of age-matched healthy controls (n = 30). MAIN OUTCOMES AND RESULTS: All but two patients completed the training, indicating that the method is feasible with a wide range of patients with stroke. The amount patients gained on the training task was similar to that of healthy controls (that is, the Group × Time interactions were by-and-large not significant), suggesting that patients with stroke might benefit to the same degree as healthy adults from this training. Both groups showed some indication of transfer to the non-trained backward digit span task and visuospatial memory. CONCLUSIONS: These findings show that repetition-lag memory training is a possible approach with patients with stroke to enhance recollection. Further research on the method's efficacy and effectiveness is warranted.
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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 | 0.000 | 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) | 0.002 | 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".