Normative data of the Dutch Oxford Cognitive Screen (OCS-NL): a stroke-specific instrument
Bibliographic record
Abstract
Objective: The Oxford Cognitive Screen (OCS) is a short screening instrument for cognitive disorders after stroke. The English version of the OCS has good reliability and validity (Demeyere et al., 2015, Psychological Assessment). The OCS briefly assesses disorders in the cognitive domains most often affected by stroke, that is: attention, memory, language, numerical cognition and praxis. Test administration takes 15 to 20 minutes. The test has been designed to be suitable for patients with aphasia, by providing multiple-choice response options, and for patients with hemispatial neglect or visual field deficits, by aligning test items in the center of the visual field. Since there are no Dutch screening instruments available specifically tailored to the stroke population, we recently developed a Dutch translation and adaptation of the OCS (OCS-NL). We are currently testing the reliability and validity of this new screening instrument. Participants and Methods: We have collected normative data in Flanders of a large group of neurologically healthy individuals (N=60), ranging between ages 18 until 90 years old. Participants were administered a cognitive screen in two sessions with one week in between the two sessions. In each session they were administered one of two parallel versions of the Montreal Cognitive Assessment (MOCA) and one of two parallel versions of the OCS-NL. In addition to neuropsychological screening, an elaborate health questionnaire was administered. Results: The neurologically healthy sample performed at ceiling for many subtests of the OCS-NL, which is comparable to the results of the original OCS. Conclusions: The OCS-NL can become a valuable instrument for cognitive screening after stroke in the Dutch speaking areas. Validation of the OCS-NL in the stroke population will provide meaningful information for its use in clinical practice.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".