True functional ability of chronic stroke patients
Bibliographic record
Abstract
BACKGROUND: There is a paucity of information regarding visuospatial (VS) and visuomotor (VM) task performance in patients with chronic right fronto-parietal lobe stroke, as the majority of knowledge to date in this realm has been gleaned from acute stroke patients. The goal of this paper is to determine how VS and VM performance in chronic stroke patients compare to the performance of healthy participants. METHODS: Nine patients with stroke involving the right fronto-parietal region were evaluated against match controls on neuropsychological tests and a computerized visuomotor assessment task. RESULTS: Initial evaluation indicated that performance between participant groups were relatively similar on all measures. However, an in-depth analysis of variability revealed observable differences between participant groups. In addition, large effect sizes were also observed supporting the theory that using only conventional examination (e.g., p-values) measures may result in miss-identifying crucial stroke-related differences. CONCLUSION: Through conventional evaluation methods it would appear that the chronic stroke participants had made significant functional gains relatively to a control group many years post-stroke. It was shown that the type of evaluation used is essential to identifying group differences. Thus, supplementary methods of evaluation are required to unmask the true functional ability of individuals many years post-stroke.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".