An Overview of Randomized Controlled Trials Examining Prescription and Nonprescription Pharmacological Interventions for Moderate to Severe Traumatic Brain Injury
Bibliographic record
Abstract
Objective: To characterize randomized controlled trials (RCTs) of pharmacological interventions (prescription medications, nonprescription medications, and supplements) for the management of moderate to severe traumatic brain injury (MSTBI). Data sources: Systematic searches were conducted in MEDLINE, PubMed, Scopus, CINAHL, EMBASE, and PsycINFO for RCTs up to December 2022 inclusive in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Study selection and data extraction: Inclusion criteria were RCT study design; participants’ mean age ≥ 18 years and ≥ 50% had MSTBI; examined ≥ 1 pharmacological intervention(s), either alone or in combination with other interventions. Two independent reviewers conducted Cochrane risk of bias assessment. Data synthesis: Three hundred thirteen RCTs (1978-2022) met inclusion criteria. A total of 146 unique pharmacotherapies and supplements were studied. The most frequently studied intervention was mannitol ( n = 20 RCTs). Mean sample size was 230.4 (4-12 737) and 195 studies (62.3%) were conducted in the acute phase post-MSTBI. Four hundred thirty-five unique outcome measures (OMs) were studied; the most common OMs used were Glasgow Outcome Scale (GOS) (29.4%), mortality (25.2%), and intracranial pressure (25.2%), Glasgow Coma Scale (GCS) (19.5%), and mean arterial pressure (17.3%), and heart rate (10%). Of the included studies, only 7% ( n = 22) had low risk of bias. Conclusion: The paucity of high-quality studies, variability in RCT methodology, sample sizes, and OMs utilization, as well as the low number of RCTs conducted in the subacute- and chronic-phase after injury pose a challenge for conducting meta-analyses to provide strong recommendations for informed decision-making in clinical practice.
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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 | 0.055 | 0.152 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 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".