Oxfordshire Community Stroke Project Classification Poorly Differentiates Small Cortical and Subcortical Infarcts
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Oxfordshire Community Stroke Project (OCSP) is a common clinical stroke classification tool. We evaluated the accuracy of OCSP classification with a prospective magnetic resonance imaging (MRI) study. METHODS: Stroke/transient ischemic attack patients presenting within 48 hours of onset were included in the study (n=130). Following computed tomography scan, OCSP classification, total anterior circulation infarcts (TACI), partial anterior circulation infarcts (PACI), lacunar circulation infarcts (LACI), and posterior circulation infarcts (POCI) were performed by 3 independent examiners. All patients underwent diffusion-weighted MRI with planimetric volume measurement and classification into OCSP categories, organized by lesion location. RESULTS: Patients were clinically classified as TACI (12 patients), PACI (62 patients), LACI (38 patients), and POCI (18 patients). In 101 patients with diffusion-weighted MRI lesions, correct classification rates were: TACI (83.3%), PACI (83%), LACI (39%), and POCI (86%). OCSP had the following sensitivity (SE), specificity (SP), and positive predictive value (PPV): TACI (SE, 100%; SP, 98%; PPV, 83%), PACI (SE, 73%; SP, 78%; PPV, 83%), LACI (SE, 47%; SP, 83%; PPV, 39%), and POCI (SE, 92%; SP, 98%; PPV, 86%). Sixty-one percent of patients in the LACI group had radiographic appearances consistent with PACI, and 15% of those classified as PACI had lacunar infarcts. No differences in stroke severity existed between patients classified correctly (median National Institutes of Health Stroke Scale [NIHSS]=4; interquartile range [IQR]=7) or incorrectly (median NIHSS=3; IQR=3). Patients classified correctly had larger infarct volume (median=6.75 mL; IQR=33.2) than did those who were incorrectly classified (1.86 mL; IQR=5; P=0.008). CONCLUSIONS: OCSP classification does not permit accurate discrimination between lacunar and small-volume cortical infarcts. Differential patterns of investigation for stroke etiology should not be based solely on clinical criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".