Post Stroke Visual Impairment: Interdisciplinary Collaborative Program – Canadian Perspective
Bibliographic record
Abstract
Post-stroke visual impairment (PSVI) affects more than 60% of the people who have a stroke but visual function is not routinely assessed in most stroke units. Recent high-quality research in this field has produced evidence-based assessment and management practices for PSVI. Orthoptists have integrated into the stroke care team in the United Kingdom (UK) to provide this valuable service. This paper outlines the development and implementation of the first known orthoptic PSVI service in North America. A pilot program was developed between the Ophthalmology, Neurology, and Physical Medicine and Rehabilitation Departments in Saskatoon, Saskatchewan, Canada. A new referral pathway was established to refer stroke patients with PSVI directly to an orthoptist with specialization in PSVI. Clinical findings from the first year of operation are reported. One hundred and seven visual deficits were suspected by the referring service. Visual field deficits were the most commonly reported PSVI. Diplopia was the most commonly reported oculomotor deficit in this cohort, and visual neglect/inattention was the most common visual perceptual deficit reported. Reading issues were the most commonly reported functional issues. Following assessment by an orthoptist, 45 additional visual deficits were identified and addressed. Stroke patients require assessment and treatment for PSVI. Orthoptists are perfectly suited to PSVI assessment and management. There is an opportunity to increase orthoptists' scope of practice in North America to ensure stroke patients receive specialized vision assessment and treatment.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".