A model‐based approach to limb apraxia in Alzheimer's disease
Bibliographic record
Abstract
Limb apraxia is a neurological deficit characterized by an inability to pantomime and/or imitate gestures, which can result from neurodegenerative disorders such as Alzheimer's disease (AD). The major goal of the study was to describe comprehensively the apraxia deficits observed in AD patients and to relate those deficits to general cognitive status, measures of daily activity, and other neuropsychological measures. Limb apraxia was assessed on a variety of conceptual and gesture production tasks in 30 AD patients. As a group, AD patients were impaired across gesture production tasks: of note was the greater impairment in imitation, as opposed to pantomime, which was especially pronounced when patients were imitating with a delay. Imitation performance was best predicted by measures of visuospatial processing, while imitation with delay was best predicted by measures of working memory. In addition, pantomime in response to pictures of tools was less accurate than Pantomime to Verbal Command and holding the tool during performance did not decrease the participants' impairment, while introducing a verbal cue during imitation increased the severity of deficits. Furthermore, investigation into patterns of deficits clearly demonstrated that the nature of limb apraxia deficits observed in AD can be quite heterogeneous and that dissociations between the conceptual and the production system exist. Finally, we also report on significant correlations between general cognitive status and limb apraxia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".