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 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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".