Onset, Risk Factors, and Impact of Delirium in Patients with Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Delirium is a commonly reported acute care adverse event in patients with traumatic spinal cord injury (TSCI), but studies specifically investigating it in this population are lacking. The purpose of this study was to characterize the onset, risk factors, and impact of delirium in patients with TSCI. Patients discharged between 2008 and 2010 were identified from a prospective registry in an acute SCI center. Controls were matched to delirium cases based on date of discharge from acute care. Patient characteristics, risk factors, and the hospital unit (intensive care, spine step-down, spine ward) in which delirium occurred were collected retrospectively. Length of stay (LOS) was calculated and compared between cases and controls. A predictive model was built for patient characteristics and risk factors associated with delirium using logistical regression. There were 192 patients identified from the study group; 34 (17.7%) were delirium cases and 34 were selected as controls. Most delirious episodes were reported during high acuity care (76.5%). The median time interval between injury and delirium identification was 8.5 days (interquartile range=5-31). Age at injury (p<0.01) and initial motor score (p<0.05) were significantly associated with delirium. Patients with delirium had significantly greater LOS than controls (median LOS=46.9 vs. 15.3 days respectively, p<0.0001). Elderly patients who sustain a TSCI and have a low motor score on admission are at increased risk of delirium. These results could contribute to the development of a screening program to address the problem of delirium in the TSCI population.
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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 | 0.004 |
| 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 | 0.001 |
| 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".