P.068 Post-stroke orthoptic clinic assessment improves patient perceived quality of life
Bibliographic record
Abstract
Background: Visual impairment can impact 70% of individuals who have experienced a stroke. Identification and remediation of visual impairments can improve overall function and perceived quality of life. Our project aimed to improve visual assessment and timely intervention for patients with post-stroke visual impairment (PSVI). Methods: We conducted a quality improvement initiative to create a standardized screening and referral process for patients with PSVI to access an orthoptist. Post-stroke visual impairment was identified using the Visual Screen Assessment (VISA) tool. Patients filled out a VFQ-25 questionnaire before and after orthoptic assessment, and differences between scores were evaluated. Results: Eighteen patients completed the VFQ-25 both before and after orthoptic assessment. Of the vision related constructs, there was a significant improvement in reported outcomes for general vision (M=56.9, SD=30.7; M=48.6, SD=16.0), p=0.002, peripheral vision (M=88.3, SD=16; M=75, SD=23.1), p= 0.027, ocular pain (M=97.2, SD=6.9; M=87.5, SD=21.4), p=0.022, near activities (M=82.4, SD=24.1; M=67.8, SD=25.6), p<0.001, social functioning (M=90.2, SD=19; M=78.5, SD=29.3), p=0.019, mental health (M=84.0, SD=25.9; M=70.5, SD=31.2), p=0.017, and role difficulties (M=84.7, SD=26.3; M=67.4, SD=37.9), p=0.005. Conclusions: Orthoptic assessments for those with PSVI significantly improved perceived quality of life in a numerous vision related constructs, suggesting it is a valuable part of a patient’s post-stroke recovery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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".