Real-life cognitive functioning after acquired brain injury: An experience sampling study
Bibliographic record
Abstract
Background Acquired brain injury (ABI) often leads to cognitive impairments, typically measured with cognitive tests in controlled environments. However, cognitive functioning in daily life is likely to fluctuate. This study explored the relationship between traditional subjective and objective cognitive measures and cognitive variability throughout the day and hypothesized that retrospective complaints are more strongly associated with momentary than baseline objective performance, and that greater cognitive variability would relate to higher subjective complaints. It also explored within-person associations between momentary cognitive performance and momentary affect, fatigue, social company and setting. Methods We conducted an experience sampling method study among 41 ABI patients. Baseline measures (Checklist for Cognitive and Emotional consequences following stroke, Montreal Cognitive Assessment and Digit Symbol Substitution Test) were administered during a briefing session. Participants responded to seven semi-random daily beeps over seven days using a mobile app. Momentary cognitive performance was assessed with a short digital version of the Digit Symbol Substitution Test. Results On average, participants completed 79% of beeps. Contrasting our hypothesis, cognitive variability was not associated with retrospective cognitive complaints or baseline cognitive performance. Multilevel analyses showed that momentary concentration complaints (β = 0.10, p = 0.02), fatigue (β = 0.10, p = 0.01) and being away from home (β = 0.23, p = 0.04) were associated with lower momentary cognitive performance. Affect and social company did not significantly impact performance. No between-person effects were found. Conclusions Momentary concentration complaints more accurately reflect real-time cognitive performance than retrospective questionnaires. Momentary fatigue impacts real-life cognition after ABI. Measuring cognitive variability can contribute to understanding the cognitive consequences of ABI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".