Overview of randomized controlled trials of moderate to severe traumatic brain injury: A systematic review
Bibliographic record
Abstract
BACKGROUND: Given the complexity of post-TBI medical, surgical, and rehabilitative care, research is critical to optimize interventions across the continuum of care and improve outcomes for persons with moderate to severe TBI. OBJECTIVE: To characterize randomized controlled trials (RCTs) of moderate to severe traumatic brain injury (TBI) in the literature. METHOD: Systematic searches of MEDLINE, PubMed, Scopus, CINAHL, EMBASE and PsycINFO for RCTs up to December 2022 inclusive were conducted in accordance with PRISMA guidelines. RESULTS: 662 RCTs of 91,946 participants published from 1978 to 2022 met inclusion criteria. The number of RCTs published annually has increased steadily. The most reported indicator of TBI severity was the Glasgow Coma Scale (545 RCTs, 82.3%). 432 (65.3%) RCTs focused on medical/surgical interventions while 230 (34.7%) addressed rehabilitation. Medical/surgical RCTs had larger sample sizes compared to rehabilitation RCTs. Rehabilitation RCTs accounted for only one third of moderate to severe TBI RCTs and were primarily conducted in the chronic phase post-injury relying on smaller sample sizes. CONCLUSION: Further research in the subacute and chronic phases as well as increasing rehabilitation focused TBI RCTs will be important to optimizing the long-term outcomes and quality of life for persons living with TBI.
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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 | 0.031 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".