Applications of Machine Learning in Prognostication of Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to review the literature regarding the current state and clinical applicability of machine learning models in prognosticating the outcomes of patients with mild traumatic brain injury in the early clinical presentation. DESIGN: Databases were searched for studies including machine learning and mild traumatic brain injury from inception to March 10, 2023. Included studies had a primary outcome of predicting post-mild traumatic brain injury prognosis or sequelae. The Prediction model study Risk of Bias for Predictive Models assessment tool was used for assessing the risk of bias and applicability of included studies. RESULTS: Out of 1235 articles, 10 met the inclusion criteria, including data from 127,929 patients. The most frequently used modeling techniques were support vector machine and artificial neural network and area under the curve ranged from 0.66 to 0.889. Despite promise, several limitations to studies exist such as low sample sizes, database restrictions, inconsistencies in patient presentation definitions, and lack of comparison to traditional clinical judgment or tools. CONCLUSIONS: Machine learning models show potential in early stage mild traumatic brain injury prognostication, but to achieve widespread adoption, future clinical studies prognosticating mild traumatic brain injury using machine learning need to reduce bias, provide clarity and consistency in defining patient populations targeted and validate against established benchmarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".