Sex Differences in Moderate-to-Severe Traumatic Brain Injury Randomized Controlled Trials
Bibliographic record
Abstract
ABSTRACT Background: Understanding sex differences among persons with moderate-to-severe traumatic brain injury (TBI) is critical to addressing the unique needs of both males and females from acute care through to rehabilitation. Epidemiological studies suggest that 7 of every 10 persons with moderate-to-severe TBI are male, with females representing about 30%–33%. Objective: To examine the proportion of female and male individuals included in randomized controlled trials (RCTs) of interventions for moderate-to-severe TBI. Methods: A systematic review was conducted in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines up to and including December 2022 using MEDLINE, PubMed, Scopus, CINAHL, EMBASE and PsycINFO databases. Studies were included if they met the following criteria: (1) human participants with a mean age ≥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, number of female/male participants and time post-injury. Results: 595 RCTs met the criteria for inclusion, published between 1978 and 2022, totaling 86,662 participants. The average proportion of female participants was 23.14%, and the percentage increased a small but significant amount over time. There was a significantly lower percentage of female participants in RCTs initiated in the acute phase (≤ 1 month) when compared with RCTs conducted in the chronic phase (≥ 6 months) post-injury ( p < 0.001). Conclusions: Female participants are underrepresented in RCTs of moderate-to-severe TBI. Addressing this underrepresentation is critical to establish effective treatments for all persons with TBI.
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 | 0.121 | 0.313 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".