Unconsciousness or unresponsiveness in Akinetic Mutism? Insights from a multimodal longitudinal exploration
Bibliographic record
Abstract
The clinical assessment of patients with disorders of consciousness (DoC) relies on the observation of behavioral responses to standardized sensory stimulation. However, several medical comorbidities may directly impair the production of reproducible and appropriate responses, thus reducing the sensitivity of behavior-based diagnoses. One of these is Akinetic Mutism (AM), a rare neurological syndrome characterized by the inability to initiate volitional motor responses, sometimes associated with clinical presentations overlapping with those of DoC. Here we describe the case of a patient with large bilateral mesial frontal lesions showing a prolonged behavioral unresponsiveness and a severe disorganization of electroencephalographic (EEG) background, compatible with a vegetative state/unresponsive wakefulness syndrome (VS/UWS). By applying an unprecedented battery of multimodal longitudinal measurements encompassing spontaneous EEG, evoked potentials, event-related potentials, transcranial magnetic stimulation-evoked potentials, and structural and functional MRI, we provide (i) a demonstration of the preservation of consciousness despite unresponsiveness in the context of a complete AM, (ii) a plausible neurophysiological explanation of behavioral unresponsiveness and of its subsequent recovery during rehabilitation stay and (iii) novel insights into the relationships between DoC, AM and parkinsonism. The present case provides proof-of-principle evidence supporting the clinical utility of a multimodal hierarchical workflow combining conventional and advanced techniques to detect covert signs of consciousness in unresponsive patients.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".