Impact of cognition on test–retest reliability and concurrent validity of n-back for Chinese stroke patients
Bibliographic record
Abstract
Objective The objective of this study was the measurement of the test–retest reliability of n-back in Chinese stroke patients.Methods Seventy-five sub-acute stroke patients performed n-back twice in three days. The test–retest reliability of n-back was analyzed by correlation coefficient.Results The n-back had excellent test–retest reliability in stroke patients. Pearson or Spearman coefficients ranged from 0.81 to 0.88. The intra-class correlation coefficients ranged from 0.72 to 0.87. The Chinese version of Montreal Cognitive Assessment-Basic (MoCA-BC) score was significantly correlated with the performance of n-back. MoCA-BC and n-back accuracy were significantly related in the Mild Cognitive Impairment (MCI) group (r = 0.60 in 1-back, p = .002; r = 0.43 in 2-back, p = .040). However, MoCA-BC was correlated with reaction time (RT) in the Cognitively Normal (CN) group (r = –0.44 in 1-back, p = .003; r = –0.36 in 2-back, p = .018). The test–retest reliability of CN group was mostly higher than that of MCI group RT: 0.71–0.76 in MCI, 0.80–0.88 in CN; accuracy: 0.80–0.85 in MCI, 0.75–0.86 in CN). The practice effect was observed in the CN group instead of the MCI group.Conclusions This study indicated that the test–retest reliability of n-back was high in stroke patients. N-back was correlated with cognition. It was preferable to conduct subgroup analyses according to the level of cognitive assessment of patients with stroke.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".