The Frequency, Clinical Correlates, and Mechanism of Anosognosia after Stroke
Bibliographic record
Abstract
OBJECTIVE: To review the frequency, clinical correlates, and mechanism of anosognosia after stroke. METHODS: We searched the most recent relevant literature on anosognosia after stroke and carried out a critical analysis of the main findings. RESULTS: Anosognosia is present in about 10% of acute stroke patients and its diagnosis is relatively simple. Nevertheless, a valid and reliable standardization of diagnostic instruments and criteria for research purposes is more difficult to achieve. This limitation may partially account for various instruments available to assess anosognosia and the different strategies used to diagnose this phenomenon. Anosognosia is a fleeting phenomenon and chronic cases are infrequent. There is a robust association between anosognosia and right-hemisphere lesions involving cortical (insular, temporal, and parietal lobes) and subcortical structures (thalamus and basal ganglia). The main clinical correlates of anosognosia are the presence of neglect, cognitive deficits, previous strokes, and older age. Anosognosia has a negative impact on the rehabilitation of stroke patients. The mechanism of anosognosia remains unknown but was explained as owing to psychological denial, disconnection between left and right hemispheres, and dysfunction of a system that monitors the intention to move and actual movements. CONCLUSION: Anosognosia is a relatively frequent complication of acute stroke and may become an excellent model to understand the mechanism of human awareness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".