Rehabilitation Oculomotor Screening Evaluation (ROSE)—A Proof-of-Principle Study for Acquired Brain Injuries
Bibliographic record
Abstract
Background/Objectives: Acquired brain injury (ABI) is a major cause of global disability. Many ABI patients exhibit oculomotor dysfunctions that impact their daily life and rehabilitation outcomes. Current clinical tools for oculomotor function (OMF) assessment are limited in their usability. In this proof-of-principle study, we aimed to develop an efficient tool for OMF screening and to assess the feasibility, acceptability, and relevance in a small sample of ABI and control participants. Methods: We created the Rehabilitation Oculomotor Screening Evaluation (ROSE) by reviewing existing OMF assessments. ROSE was pilot-tested on ABI patients (n = 10) and age-matched controls (n = 10). Data regarding the characteristics of the assessment, such as the duration, level of participant comprehension, and participant experience were also collected. Results: ROSE takes <20 min (x¯ = 12.5), is easy to complete (agreement x¯ = 4.6/5), and is well-accepted (x¯ = 4.8/5). Patients scored higher in all subtests and total score (x¯ = 34.8 for ABI vs. 8.9 for controls). Most subtests did not provoke any symptoms, especially for controls. There were no significant between-group differences in symptom provocation. This proof-of-principle study shows that ROSE is feasible, acceptable, and relevant for adult ABI patients. Conclusions: ROSE needs further evaluation for reliability testing and validation in larger samples and diverse neurological conditions. Establishing norms for various ages, sexes, and populations should be considered for the deployment of ROSE as an OMF clinical tool.
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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 | 0.019 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".