Predicting Functional Outcome One Year After Traumatic Brain Injury With CT and MRI Findings
Bibliographic record
Abstract
Background: The aim of this study was to evaluate the relative and combined impact of computed tomography (CT) and Magnetic resonance imaging (MRI) on functional outcome one year after traumatic brain injury (TBI). Methods: This study was a prospective, population-based study of 87 patients with a Glasgow Coma Scale (GCS) score ≤ 12 who were injured in 2005 - 2007 and hospitalized at Trauma Referral Centre in Eastern Norway. CT performed within 24 h post-injury was classified by Marshall classification scale. MRI performed one year post-injury was classified based on the presence or absence of diffuse axonal injuries (DAIs). The Glasgow Outcome Scale Extended (GOSE) was used as an outcome measure at the one-year follow-up. The predictions models were adjusted for clinical variables known to affect functional outcome. Results: Using CT, s mall lesions (Marshall group 2) were observed in 37 (42%) patients. Signs of increased intracranial pressure (Marshall groups 3 - 4) were present in 33 (38%) patients. Using MRI, DAI lesions were found in 70% of patients. In the linear regression analysis that explored relative impact of CT, CT was a significant predictor of GOSE (p = 0.08). Conclusions: The relative impact of CT findings of intracranial lesions in the acute settings and one-year MRI findings of DAI on functional outcome underscored the importance of using neuroimaging techniques when predicting functional outcomes after TBI. The better predictive value of MRI suggest that the detailed information about pathological brain lesions shown on late MR may help clinicians to administer more appropriate rehabilitation treatments to patients who are predicted to have a worse outcome at the one-year follow-up. doi: http://dx.doi.org/10.4021/jnr133w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".