Disorders of consciousness diagnosis, interventions, and prognostication for the intensivist: Report of the 2025 ISICEM roundtable
Bibliographic record
Abstract
Disorders of consciousness (DoC) represent a spectrum of clinical conditions, including coma, unresponsive wakefulness syndrome, and the minimally conscious state, which may result from structural and non-structural brain injuries due to trauma, stroke, anoxia, infections of the brain, and other causes. Clinical management of patients with DoC is especially challenging in the critical care environment, where the level of consciousness, a key factor in determining the trajectory of recovery, may be obscured by sedation, analgesia, and other confounders. The 2025 International Symposium on Intensive Care and Emergency Medicine hosted a Roundtable of 18 expert clinicians and researchers to synthesise and discuss the latest evidence on acute DoC epidemiology, diagnosis, treatment, and prognosis. Here, we summarise the output of the Roundtable in the format of a roadmap with six steps related to identifying patients with DoC, assessing for and treating confounders, establishing a diagnosis and prognosis, selecting interventions, and effectively communicating with family. This roadmap provides practical, evidence-informed guidance to help intensivists navigate diagnosis, treatment, and prognostication in patients with acute DoC. Advances in structural and functional neuroimaging, electrophysiology, and blood-based biomarkers offer promise for refined diagnostics and prognostication, though their clinical translation remains limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".