Brain Responses to Propofol in Advance of Recovery from Coma and Disorders of Consciousness: A Preliminary Study
Bibliographic record
Abstract
Abstract Rationale Predicting recovery of consciousness in unresponsive, brain-injured individuals has crucial implications for clinical decision-making. Propofol induces distinctive brain network reconfiguration in the healthy brain as it loses consciousness. In patients with disorders of consciousness, the brain network’s reconfiguration to propofol may reveal the patient’s underlying capacity for consciousness. Objectives To design and test a new metric for the prognostication of consciousness recovery in disorders of consciousness. Methods Using a within-subject design, we conducted an anesthetic protocol with concomitant high-density EEG in 12 patients with a disorder of consciousness after a brain injury. We quantified the reconfiguration of EEG network hubs and directed functional connectivity before, during, and after propofol exposure and obtained an index of propofol-induced network reconfiguration: the adaptive reconfiguration index. We compared the index of patients who recovered consciousness 3 months after EEG (n = 3) to that of patients who did not recover or remained in a chronic disorder of consciousness (n = 7) and conducted a logistic regression to assess prognostic accuracy. Measurements and Main Results The adaptive reconfiguration index was significantly higher in patients who later recovered full consciousness (U value = 21, P = 0.008) and able to discriminate with 100% accuracy whether the patient recovered consciousness. Conclusions The adaptive reconfiguration index of patients who recovered from a disorder of consciousness at 3-month follow-up was linearly separable from that of patients who did not recover or remained in a chronic disorder of consciousness on the single-subject level. EEG and propofol can be administered at the bedside with few contraindications, affording the adaptive reconfiguration index tremendous translational potential as a prognostic measure of consciousness recovery in acute clinical settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".