Stockholm Score of Lesion Detection on Computed Tomography following Mild Traumatic Brain Injury (SELECT-TBI) Study: Pilot Analysis and Statistical Analysis Plan
Bibliographic record
Abstract
BACKGROUND: Mild traumatic brain injury (mTBI) is a common cause of emergency department visits. Only a small percentage of mTBI patients develop an intracranial lesion (ICL) and even fewer will require neurosurgical intervention due to their injury. The Stockholm Score of Lesion Detection on Computed Tomography following Mild Traumatic Brain Injury (SELECT-TBI) study aims to provide a data-driven approach to estimate individualized risk for traumatic ICL and clinically significant lesions in mTBI patients. OBJECTIVE: To provide a statistical analysis plan and pilot data analysis before completion of data collection, as pre-planned in the published study protocol. METHODS: Retrospective study of patients ≥ 15 years old who underwent a computed tomography (CT) scan for their mTBI in Stockholm, Sweden, between 2015-2020. Up to 73 variables were collected for each patient. Data analysis of the first 5 000 patients in the cohort was conducted to develop preliminary prediction models using Lasso regression, general linear model and random forest and to perform an optimal population analysis to determine whether the final sample size would be sufficient. RESULTS: Six data selection strategies were tested, and area under the curve (AUC) receiver operator characteristic (ROC) curves were generated with a 4:1 training/validation data segmentation. The best-performing model was the Lasso regression model which achieved an AUC of 0.807 for any ICL and 0.903 for clinically significant ICL (accuracy of 70% and 97.7%, and Brier scores of 0.3 and 0.023 respectively). Clinical variables identified as key features across all models were Glasgow Coma Scale, signs of basilar skull fracture, trauma mechanism, and vomiting, each with an importance score greater than 0.1 (explaining more than 10% model variance). Finally, the highest end prediction of the necessary population size was found to be 29 667 patients. CONCLUSION: Our preliminary results demonstrate the potential for a data-driven approach to generate personalized risk stratification tools. With a final cohort size expected to exceed 40 000 patients, we anticipate being able to create more granular models optimized for integration into clinical decision-making. STUDY REGISTRATION: ClinicalTrials.gov NCT04995068.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".