What is the physiological time to recovery after concussion? A systematic review
Bibliographic record
Abstract
AIM OR OBJECTIVE: The aim of this study is to consolidate studies of physiological measures following sport-related concussion (SRC) to determine if a time course of postinjury altered neurobiology can be outlined. This biological time course was considered with respect to clinically relevant outcomes such as vulnerability to repeat injury and safe timing of return to physical contact risk. DESIGN: Systematic review. DATA SOURCES: PubMed, CINAHL, Cochrane Central, PsychINFO. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Studies were included if they reported original research on physiological or neurobiological changes after SRC. Excluded were cases series <5 subjects, reviews, meta-analyses, editorials, animal research and research not pertaining to SRC. RESULTS: A total of 5834 articles were identified, of which 80 were included for full-text data extraction and review. Relatively few longitudinal studies exist that follow both physiological dysfunction and clinical measures to recovery. SUMMARY/CONCLUSIONS: Modalities of measuring physiological change after SRC were categorised into the following: functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, cerebral blood flow, electrophysiology, heart rate, exercise, fluid biomarkers and transcranial magnetic stimulation. Due to differences in modalities, time course, study design and outcomes, it is not possible to define a single 'physiological time window' for SRC recovery. Multiple studies suggest physiological dysfunction may outlast current clinical measures of recovery, supporting a buffer zone of gradually increasing activity before full contact risk. Future studies need to use generalisable populations, longitudinal designs following to physiological and clinical recovery and careful correlation of neurobiological modalities with clinical measures.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".