Racial and Ethnic Differences in Traumatic Brain Injury Outcomes From 2009 to 2023: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: In 2009, Gary and colleagues reviewed prior research examining racial and ethnic differences in outcomes after traumatic brain injury (TBI). Over the past 15 years, advances in research and changes in the demographic composition of the United States warrant a comprehensive understanding of racial and ethnic disparities after TBI. OBJECTIVE: A systematic review will be conducted to examine racial and ethnic differences in TBI outcomes from 2009 to 2023. METHODS: Preliminary searches and study screening processes will identify relevant English-language articles published from January 2009 to December 2023 using the CINAHL, Gale OneFile, PsycINFO (Ovid), and PubMed electronic databases. Relevant articles will include quantitative or mixed method approaches, involve individuals with TBI or their caregivers, and compare 2 or more groups by race or ethnicity on post-TBI outcomes. Quality will be assessed using the Newcastle-Ottawa Scale. This systematic review protocol was developed following PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. Results will be summarized, and a subgroup analysis may be conducted based on demographics (eg, age, gender, or sex). RESULTS: We have already identified abstracts using the search strategy for all 4 of the included electronic databases. We recently updated the search and will begin abstract screening of the additional abstracts identified from the last search completed in January 2024. This systematic review is anticipated to be completed by fall 2024, and its findings will be disseminated to the scientific community, persons with TBI, caregivers, and the lay audience. CONCLUSIONS: This systematic review will advance our understanding regarding outcome disparities among minoritized individuals with TBI, examine progress over the past 15 years in minimizing barriers encountered by these racial and ethnic groups, and provide professionals with a roadmap illustrating existing gaps in rehabilitation care, making way for further development and implementation of evidence-based interventions to improve health equity in TBI outcomes. TRIAL REGISTRATION: PROSPERO CRD42023394529; https://tinyurl.com/53mtcz9b. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/58763.
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 | 0.077 | 0.100 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.075 | 0.011 |
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".