A-136 The Transfer of Cognitive Training to Real-World Functioning: Results of our First Six Pilot Subjects at Six-Month Follow-Up
Bibliographic record
Abstract
Abstract Objective Currently, the majority of cognitive training research measures treatment efficacy using in-laboratory measures, with minimal focus on real-world treatment changes. This case series demonstrates the feasibility of transferring cognitive improvements from the laboratory into the everyday life setting. Method This case series includes 6 chronic post-stroke participants; mild to moderate cognitive impairment. The intervention combines cognitive training with behavioral techniques, known as the Transfer Package (TP). The TP involves components that target functionality on IADLs in the real-world. Performance on cognitively-based IADLs in the real world are measured pre-treatment, post, and 6-month follow-up. Measures of real-world ability are the: Canadian Occupational Performance Measure (COPM), Cognitive Task Activity Log (CTAL) and Inventory of Improved and New Abilities (INCA). In-laboratory measures included the D-KEFS and Timed IADL assessments. Results The real-world outcome measures used in this study were the COPM and two measures developed for this study, the CTAL and INCA. The mean change from pre to post on the COPM Performance Scale was 2.18 (SD = 1.33) and the mean change on the COPM Satisfaction Scale was 2.70 (SD = 1.27). The mean change on the CTAL was 1.96 (SD = 0.93). On the INCA, the mean number of improved real-world cognitive activities was 11.8 (SD = 4.9) and the mean number of new cognitive activities was 7.6 (SD = 3.9). Follow-up reported near-perfect retention on CTAL and continued improvement on the INCA. There were minimal changes on in-laboratory measures. Conclusions This case series provides a framework for achieving the transfer of cognitive training treatment effects in the real-world life situation by overcoming behavioral barriers to functioning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".