Systemic Lisbon Battery: Normative Data for Memory and Attention Assessments
Bibliographic record
Abstract
BACKGROUND: Memory and attention are two cognitive domains pivotal for the performance of instrumental activities of daily living (IADLs). The assessment of these functions is still widely carried out with pencil-and-paper tests, which lack ecological validity. The evaluation of cognitive and memory functions while the patients are performing IADLs should contribute to the ecological validity of the evaluation process. OBJECTIVE: The objective of this study is to establish normative data from virtual reality (VR) IADLs designed to activate memory and attention functions. METHODS: A total of 243 non-clinical participants carried out a paper-and-pencil Mini-Mental State Examination (MMSE) and performed 3 VR activities: art gallery visual matching task, supermarket shopping task, and memory fruit matching game. The data (execution time and errors, and money spent in the case of the supermarket activity) was automatically generated from the app. RESULTS: Outcomes were computed using non-parametric statistics, due to non-normality of distributions. Age, academic qualifications, and computer experience all had significant effects on most measures. Normative values for different levels of these measures were defined. CONCLUSIONS: Age, academic qualifications, and computer experience should be taken into account while using our VR-based platform for cognitive assessment purposes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".