Comparing Randomized Controlled Trials of Moderate to Severe Traumatic Brain Injury in Lower to Middle Income Countries Versus High Income Countries
Bibliographic record
Abstract
Outcomes from traumatic brain injury (TBI) including death differ significantly between high-, middle-, and low-income countries. Little is known, however, about differences in TBI research across the globe. The objective of this article was to examine randomized controlled trials (RCTs) of moderate-to-severe TBI in high-income countries (HICs) compared with low- and middle-income countries (LMICs), as defined by the World Bank income per capita cutoff of $13,205 US dollars. A systematic review was conducted for articles published in the English language to December 2022 inclusive using MEDLINE, PubMed, Scopus, CINAHL, EMBASE, and PsycINFO in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Inclusion criteria: (1) human participants with a mean age of ≥18 years; (2) ≥50% of the sample had moderate to severe TBI; and (3) the study design was a RCT. Data extracted included author, year, country, sample size, primary focus (medical/surgical management or rehabilitation), injury etiology, time post-injury, and indicator(s) used to define TBI severity. There were 662 RCTs (published 1978-2022) that met inclusion criteria comprising 91,946 participants. There were 48 countries represented: 30 HICs accounting for 451 RCTs (68.1%) and 18 LMICs accounting for 211 RCTs (31.9%). The 62.6% of RCTs from LMICs were conducted in the acute phase post-injury (≤1 month) compared with 42.1% of RCTs from HICs. Of RCTs from LMICs, 92.4% focused on medical/surgical management compared with 52.5% from HICs. Since 2016, more RCTs have been conducted in LMICs than in HICs, indicating the importance of better understanding this pattern of research output.
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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 | 0.043 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".