Translating Biomarkers Research to Clinical Care: Applications and Issues for Rehabilomics
Bibliographic record
Abstract
Traumatic brain injury is a leading cause of morbidity and mortality in adults and children in the United States. Despite steady improvement in our understanding of the pathophysiology of acquired brain injuries, there has been remarkably little improvement in brain injury therapies and/or pharmacologic treatments over the past decade. One of the reasons may be the inability to properly stratify subjects for clinical trials and/or to have real-time assessment of the effectiveness of a given intervention. It has been recognized for several decades that serum biomarkers may allow for more objective subject stratification as well as act as surrogate markers of treatment efficacy. Despite numerous studies, however, biomarkers are not currently part of clinical practice in either acquired brain injury or other neurologic or musculoskeletal disorders. The goals of this review article, therefore, are to use traumatic brain injury as a example to discuss the use of biomarkers in clinical and randomized controlled trials; to briefly discuss the field of neuroproteomics and its interface with neurologic interventions; and to provide an overview of the collaborative pathway between academia and industry, which needs to be an integral part of the translation of biomarkers from the bench to the bedside in any clinical population. Introduction of the concept of rehabilomics and implications of biomarker use for the physical medicine and rehabilitation physician also are discussed.
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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 | 0.272 | 0.390 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".