P.084 Pilot Program to Determine Impact of an Orthoptic Clinic on Patient Perceived Quality of Life of Stroke Patients
Bibliographic record
Abstract
Background: Visual impairment exists for an estimated 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 aims 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 assessed by way of the Visual Screen Assessment (VISA) tool, administered by an occupational therapist. Patients filled out a VFQ-25 questionnaire before and after orthoptic assessment and intervention. The VFQ-25 is a validated post-stroke survey assessing a patient’s perceived quality of life. Differences between pre- and post-orthoptic assessment scores will be evaluated. Results: Data collection currently ongoing.The benefits of a standardized screen for PSVI, standardized referral to, and experience with an orthoptist assessment will be determined. Learnings gained will also inform how we can expand the program to benefit a wider demographic of patients. Conclusions: The data gathered and the subsequent analysis will be instrumental in guiding ongoing improvement initiatives for patients with PSVI.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.009 | 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".