5.13 We cannot translate evidence into clinical practice unless we know how non-pharmacological interventions following concussion are described. A systematic review
Bibliographic record
Abstract
Objective Examine how well non-pharmacological interventions are reported following concussion and whether reporting completeness has improved over time. Design Systematic review. Data Sources MEDLINE, Embase, PsycInfo, CINAHL, Web of Science up to May 2022. Eligibility Criteria RCTs in English or French examining non-pharmacological interventions following concussion. We contacted authors to provide unreported information. Two reviewers independently rated reporting completeness using Template for Intervention Description and Replication (TIDieR), Consensus on Exercise Reporting Template (CERT), and international Consensus on Therapeutic Exercise aNd Training (i-CONTENT) checklists. Risk of bias was assessed with the Cochrane RoB- 2 Tool. Main Results We screened 7456 studies and included 89 RCTs (n=46 high risk-of-bias), representing 9714 participants with concussion. Studies examined 11 different interventions, including sub- symptom threshold aerobic exercise, cervicovestibular therapy, physical and/or cognitive rest, vision therapy, education, psychotherapy, hyperbaric oxygen therapy, transcranial magnetic stimulation, blue light therapy, osteopathic manipulation, and head/neck cooling. The percentage of items completely reported was 80% (95%CI,79.5–80.5) (TIDieR), 83% (95%CI,81.7- 84.3) (CERT), and 81%(95%CI,80.7–81.3) (i-CONTENT). All studies reported TIDieR items 1, Brief name, 4, What procedures, 8, When and how much, and i-CONTENT item 1, Patient selection. The item reported most commonly on the CERT was 14a, Generic or tailored (89%). TIDier items 10, Modifications (33%;95%CI,32.9–33.1) and 11, How well (planned adherence) (42%;95%CI,41.9–42.1) were the least reported. Only CERT reporting completeness increased over 26 years. Conclusions Non-pharmacological interventions following concussion are moderately-well reported and of high risk-of-bias. Incomplete published description of interventions potentially limits replication of findings and translation of evidence into clinical practice.
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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 | 0.202 | 0.608 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".