Cooking breakfast after a brain injury
Bibliographic record
Abstract
Acquired brain injury (ABI) often compromises the ability to carry out instrumental activities of daily living such as cooking. ABI patients' difficulties with executive functions and memory result in less independent and efficient meal preparation. Accurately assessing safety and proficiency in cooking is essential for successful community reintegration following ABI, but in vivo assessment of cooking by clinicians is time-consuming, costly, and difficult to standardize. Accordingly, we examined the usefulness of a computerized meal preparation task (the Breakfast Task; Craik and Bialystok, 2006) as an indicator of real life meal preparation skills. Twenty-two ABI patients and 22 age-matched controls completed the Breakfast Task. Patients also completed the Rehabilitation Activities of Daily Living Survey (RADLS; Salmon, 2003) and prepared actual meals that were rated by members of the clinical team. As expected, the ABI patients had significant difficulty on all aspects of the Breakfast Task (failing to have all their foods ready at the same time, over- and under-cooking foods, setting fewer places at the table, and so on) relative to controls. Surprisingly, however, patients' Breakfast Task performance was not correlated with their in vivo meal preparation. These results indicate caution when endeavoring to replace traditional evaluation methods with computerized tasks for the sake of expediency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".