Psychometric Properties of the Chinese (Putonghua) Version of the Oxford Cognitive Screen (OCS-P) in Subacute Poststroke Patients without Neglect
Bibliographic record
Abstract
Background. Oxford Cognitive Screen is designed for assessing cognitive functions of poststroke patients. This study was aimed to assess the psychometric properties of the Chinese (Putonghua) version of the Oxford Cognitive Screen-Putonghua (OCS-P) for use among poststroke patients without neglect. Methods. Expert review panel evaluated content validity of the Chinese-translated items. After pilot tested the translated items, the patients and healthy participants completed the OCS-P as well as the Montreal Cognitive Assessment (MoCA-ChiB) and Goldenberg’s test. A group of patients completed OCS-P for the second time within seven days. Data analyses included confirmatory factor analysis, item difficulty and item-total correlation, inter- and intrarater reliability, internal consistency, and between-group discrimination. Results. One hundred patients and 120 younger ( n=60 ) or older ( n=60 ) healthy participants completed all the tests. Modifications were required for items in the “Picture Naming”, “Orientation”, and “Sentence Reading” subscales. Confirmatory factor analysis revealed a three-factor structure for the OCS-P subscales. The internal consistency coefficients for the three identified test dimensions were 0.30 to 0.52 (Cronbach’s alpha). Construct validity coefficients between the OCS-P and MoCA-ChiB subscales were 0.45 < r < 0.79 ( p<0.001 ) and the “Praxis” subscale of OCS-P and Goldenberg’s test was r=0.72 ( p<0.001 ). The interrater reliability coefficients for the subscales were in general higher than the intrarater reliability coefficients. The “Picture Naming” and “Numerical Cognition” subscales were the most significant ( p=0.003 ) for differentiating patient participants from their older healthy counterpart. Conclusion. This study generated satisfactory evidence on the content validity, substantive validity, construct validity, inter- and intrarater reliability, and known-group discrimination of the OCS-P. They support its application among poststroke patients who speak Putonghua. Future studies could review the existing five-dimension domains for improving its structural validity and internal consistency as well as generate evidence of the OCS-P for use among the poststroke patients with neglect.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".