A Retrospective Review on Post Traumatic Agitation Predictors in Hospitalized Patients with Acute Traumatic Brain Injury
Bibliographic record
Abstract
Objectives: To determine if socio-demographic, medical and radiological variables have an impact on the risk of developing post-traumatic agitation in patients with mild complex to severe Traumatic Brain Injury (TBI). In addition, determine prognosis using the Extended Glasgow Outcome Scale (GOS-E), length of stay (LOS) and orientation at discharge of agitated patients with TBI.Methods: A retrospective observational study of all 778 patients admitted to the Montreal General Hospital, a tertiary specialized trauma centre, following a TBI that occurred between 2013 and 2015. Data was collected from the national trauma registry and TBI program database. Independent variables collected were socio-demographic, clinical, and neurological information. Dependent variables were LOS in days, non-pharmacological treatment, GOS-E at discharge, and discharge destination.Results: 55 patients (7.1%) suffered from post-traumatic agitation. The group with agitation had a significantly higher proportion of men, psychiatric history and suffered in a greater proportion a moderate TBI. The median GCS was significantly lower and post traumatic amnesia was longer in subjects with agitation. A higher percentage of patients with agitation were transferred to long-term care (LTC) facilities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".