Effectively capturing memory deficits: New advances in the use of the In-out-Test for cognitive Test post-stroke
Bibliographic record
Abstract
Objective This study evaluated the reliability and validity of the In-Out-Test for detecting episodic memory deficits in stroke patients and explored its potential as a clinical test.Methods A total of 75 stroke patients and 120 healthy controls underwent tests, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Picture-Based Memory Impairment Screen (PMIS), and In-Out-Test. Reliability metrics (Cronbach’s α, inter-scorer reliability, test-retest reliability), criterion validity, corrected item-total correlation, hierarchical regression analysis and ROC curve analysis were performed to determine the sensitivity and specificity of the In-Out-Test.Results Stroke patients scored lower across all tests (p < 0.001), with the largest difference in the In-Out-Test (d = 0.99). The In-Out-Test correlated strongly with other cognitive tests (r = 0.79–0.85 in stroke patients; r = 0.66–0.78 in controls). It explained an additional 4.5% of variance in MoCA-MIS scores (p < 0.001). Reliability was high (Cronbach’s α = 0.835; inter-rater ICCs = 0.911–0.925; test-retest ICCs = 0.764–0.802). ROC analysis showed an AUC of 0.747, with a sensitivity of 0.708 and specificity of 0.680 at a cutoff of 10.5.Conclusion Preliminary findings indicated that the In-Out-Test showed potential in detecting episodic memory impairments in stroke patients, warranting further validation in larger cohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".