The Oxford Cognitive Screen (OCS-AU): Sensitivity and Specificity of a Stroke-Specific Cognitive Screening Tool
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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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it