Pharmacological therapies for early and long-term recovery in disorders of consciousness: current knowledge and promising avenues
Bibliographic record
Abstract
INTRODUCTION: Disorders of consciousness (DoC) are characterized by impaired arousal and/or awareness, ranging from coma to unresponsive wakefulness syndrome, minimally conscious state, and cognitive motor dissociation. Pharmacological treatment options remain limited, complicated by the heterogeneity of etiologies, such as traumatic brain injury, stroke, and infections. The lack of rigorous clinical trials has led to off-label use of treatments, often without clear mechanistic understanding, posing challenges for effective patient care. AREAS COVERED: In this perspective, the authors report on key studies concerning the effectiveness of pharmacological interventions, including dopaminergic and GABAergic agents, antidepressants, statins, and anticonvulsants, in promoting recovery of consciousness in DoC. EXPERT OPINION: Robust longitudinal clinical trials are needed, with priority given to early subacute phase intervention. Outcomes should be better defined, considering immediate responses to medication while also increasing the emphasis on long-term quality of life. Unified functional and mechanistic frameworks are needed to guide research and foster collaboration. Furthermore, a shift toward personalized medicine would benefit this heterogeneous population. Moving forward, assessing the efficacy of more unconventional or 'paradoxical' pharmacological options in treatment plans will be essential. The authors also expect an increased use of AI tools to identify factors that best predict treatment responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".