The Oxford Cognitive Screen (OCS-AU): Sensitivity and Specificity of a Stroke-Specific Cognitive Screening Tool
Bibliographic record
Abstract
Objective: The aim of this study was to evaluate the sensitivity and specificity of the Australian Oxford Cognitive Screen (OCS-AU) to detect post-stroke cognitive impairment within three months of stroke.Participants and setting: Stroke survivors (n=53) within 12 weeks of stroke were recruited from three states in Australia.Main measure: The OCS-AU.Other measures: The Montreal Cognitive Assessment (MoCA), and a comprehensive neuropsychological test battery.Design: A validation study was conducted to analyse the sensitivity and specificity of the OCS-AU in subacute stroke using a neuropsychological test battery as the reference standard. The MoCA was included for comparative purposes. Impairment was defined as failing any OCS-AU cognitive domain, scoring below 26 on the MoCA, or failing at least two domains on the neuropsychological test battery.Results: To detect impairment within individual cognitive domains, most OCS-AU scores had low sensitivity, ranging from 0.12 (Executive) to 0.92 (Spatial Attention). Specificity was higher, ranging from 0.80 (Spatial Attention) to 0.96 (Praxis). Regarding the detection of multi-domain cognitive impairments, MoCA scores showed high sensitivity (0.81) but low specificity (0.42), compared with OCS-AU lower sensitivity (0.70) but higher specificity (0.58).Conclusion: Our findings suggest that the OCS-AU has strong domain-level specificity, but may miss some individuals with mild to moderate memory and executive impairments, while the MoCA appears more sensitive to domain-general impairment, but may misclassify individuals without post-stroke cognitive impairment. Thus, the OCS-AU and MoCA could be utilised for different purposes, to leverage their strengths when addressing specific clinical needs.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".