E.04 Coma and delirium are associated with low levels of brain tissue oxygen in critically ill patients
Bibliographic record
Abstract
Background: The cause of ICU delirium is unknown. We used near infrared spectroscopy (NIRS) to measure brain tissue oxygenation (BtO2) in critically ill patients, to test the hypothesis that poor cerebral oxygen delivery contributes to ICU delirium. Methods: Adult patients were enrolled if they required mechanical ventilation for >24 hours, and/or vasoactive agents. Patients were excluded if they had previous cognitive dysfunction, brain injury on admission, or a life expectancy <24 hours. BtO2 was measured for the first 24 hours of ICU admission. The confusion assessment method-ICU (CAM-ICU) was used to screen for delirium. Participants were designated to one of three groups on the basis of their predominant neurological status (comatose, delirious, or intact). Results: To date, 47 patients have been recruited. Both delirious and comatose patients’ had significantly lower BtO2 levels compared to intact patients (P<0.001). There was a significant correlation between hemoglobin and BtO2 (R2=0.347, P<0.01). However, when correlation analysis was conducted separately amongst the three groups, the delirious patients (R2=0.485, P<0.05) were the strongest contributors to this positive correlation. Conclusions: Delirious patients exhibited the lowest BtO2 recordings and demonstrated a significant association between Hb and BtO2. This study offers potential insight into the pathophysiology of ICU delirium.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".