Overview of Medical, Surgical, and Rehabilitation Outcome Measures Used in Randomized Controlled Trials of Moderate to Severe Traumatic Brain Injury
Bibliographic record
Abstract
ABSTRACT: Optimal reporting of outcomes is critical for the interpretation of research findings. This review aimed to examine the utilization of outcome measures in randomized controlled trials of moderate to severe traumatic brain injury. Systematic searches were conducted up to December 2022 in MEDLINE, PubMed, Scopus, CINAHL, Embase, and PsycINFO, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Randomized controlled trials were included if the population studied had ≥18 yrs and ≥50% had moderate to severe traumatic brain injury. A total of 662 met inclusion criteria. There was a total of 839 unique outcome measures across all included randomized controlled trials. Of these, only 195 (23.2%) were used in ≥4 randomized controlled trials. On average, randomized controlled trials included 1.26 outcome measures (range 1-23). A total of 495 (59%) of outcome measures were classified in the recovery and rehabilitation category, and 344 (41%) in the medical and surgical measures category. There was a more equal representation of outcome measures in high-income countries compared to low to middle income countries, with the latter using fewer recovery and rehabilitation outcome measures. Outcome measures used in randomized controlled trials of moderate to severe traumatic brain injury have significant heterogeneity and variable clinical relevance, which limits the impact and generalizability of research in moderate to severe traumatic brain injury, and the ability to compare across studies.
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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 | 0.159 | 0.360 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".