Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review
Bibliographic record
Abstract
Methodological standards of existing clinical AI research remain poorly characterized and may partially explain the implementation gap between model development and meaningful clinical translation. This systematic review aims to identify AI-based methods to predict outcomes after moderate to severe traumatic brain injury (TBI), where prognostic uncertainty is highest. The APPRAISE-AI quantitative appraisal tool was used to evaluate methodological quality. We identified 39 studies comprising 592,323 patients with moderate to severe TBI. The weakest domains were methodological conduct (median score 35%), robustness of results (20%), and reproducibility (35%). Higher journal impact factor, larger sample size, more recent publication year and use of data collected in high-income countries were associated with higher APPRAISE-AI scores. Most models were trained or validated using patient populations from high-income countries, underscoring the lack of diverse development datasets and possible generalizability concerns applying models outside these settings. Given its recent development, the APPRAISE-AI tool requires ongoing measurement property assessment.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".