Effectiveness of concussion treatments in older adults: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Concussions can have significant implications on the health and quality of life of older adults. As most concussion research previously focused on children, athletes and military populations, there is a need to better understand the concussion-specific treatments for adults aged 65 and older. The aim of our systematic review is to review the existing literature on the effectiveness of concussion treatments on outcomes in adults aged 65 and older. METHODS AND ANALYSIS: This systematic review will be conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines and the Cochrane's Handbook for Systematic Reviews of Interventions. A comprehensive search of electronic databases (MEDLINE, Embase, CINAHL, AgeLine, APA PsycNet and Cochrane CENTRAL) will be performed and reference lists of included articles will be searched. We will conduct a two-step screening process and data extraction. The data analysis will integrate a narrative approach with vote-counting. The risk of bias in the included studies will be assessed, and the quality of evidence for each outcome will be evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. ETHICS AND DISSEMINATION: The results of this systematic review will contribute to the current knowledge on concussion treatments and outcomes in older adults. This work is essential for identifying effective interventions and guiding future guidelines for this under-represented population. No ethical approval is needed for the review, and we plan to present the results at an international research conference and in a peer-reviewed journal. This protocol is registered in PROSPERO (CRD # pending).
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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 | 0.094 | 0.086 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.020 | 0.016 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.098 | 0.014 |
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".