Systematic assessment of apraxia and functional predictions from the Birmingham Cognitive Screen
Bibliographic record
Abstract
OBJECTIVE: The validity and functional predictive values of the apraxia tests in the Birmingham Cognitive Screen (BCoS) were evaluated. BCoS was developed to identify patients with different forms of praxic deficit using procedures designed to be inclusive for patients with aphasia and/or spatial neglect. METHOD: Observational studies were conducted from a university neuropsychological assessment centre and from acute and rehabilitation stroke care hospitals throughout an English region. Volunteers from referred patients with chronic acquired brain injuries, a consecutive hospital sample of patients within 3 months of stroke (n=635) and a population based healthy control sample (n=100) were recruited. The main outcome measures used were the Barthel Index, the Nottingham Extended Activities of Daily Living Scale as well as recovery from apraxia. RESULTS: There were high inter-rater reliabilities and correlations between the BCoS apraxia tasks and counterpart tests from the literature. The vast majority (88.3%) of the stroke survivors were able to complete the screen. Pantomime and gesture recognition tasks were more sensitive in differentiating between individuals with left hemisphere damage and right hemisphere damage whereas the Multistep Object Use test and the imitation task had higher functional correlates over and above effects of hemiplegia. Together, the initial scores of the four tasks enabled predictions with 75% accuracy, the recovery of apraxia and independence level at 9 months. CONCLUSIONS: As a model based assessment, BCoS offers a quick and valid way to detect apraxia and predict functional recovery. It enables early and informative assessment of most stroke patients for rehabilitation planning.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".