Efficacy of restitutive interventions for oculomotor deficits in adults with mild traumatic brain injury: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Mild traumatic brain injury (mTBI) can cause persistent symptoms suggestive of oculomotor deficits. This research synthesized evidence on restitutive interventions for reducing oculomotor deficits in adults with mTBI to understand if these interventions have clinical utility for improving recovery. METHODS: Medline, EMBASE, CINHAL, PsychInfo, and Scopus, databases were searched for experimental studies published in English. We rated risk of bias (RoB) using recommended tools, and the certainty of the evidence according to GRADE guidelines. We conducted meta-analyses for similar outcomes reported in at least two studies. RESULTS: Out of 5,328 citations, 12 studies (seven case series and five crossover design), with a combined sample size of 354 participants; (43% males) met the inclusion criteria and were analyzed. The analysis revealed a trend toward improvement of oculomotor deficits and visual tasks in response to restitutive intervention. None of the studies addressed sex or gender effects. All studies had high RoB, suggesting low certainty in the reported results. DISCUSSION: Restitutive interventions may be beneficial for adults with oculomotor deficits after mTBI, however overall certainty of the evidence remains low. Future efforts must include enhancing attention to study methodology and reporting, sex and gender analyses, and reaching a consensus on outcome measures. PROSPERO REGISTRATION NUMBER: CRD42022352276.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".