An international survey of optometric management of stroke survivors
Bibliographic record
Abstract
OBJECTIVES: A survey was conducted to describe the current status of optometric awareness and involvement regarding post-stroke management as literature suggests that visual impairment often goes undiagnosed and unmanaged in stroke survivors. MATERIALS AND METHODS: A questionnaire was developed by the researchers, pilot tested by 5 other optometric researchers and 15 optometrists and modified based on the feedback. Practicing optometrists were invited to complete the anonymous online survey through optometric organizations in Canada, Hong Kong, India, UK, and US. RESULTS: Results showed that the majority of optometrists (61%) see 1-5 post-stroke patients per month, although 15% report seeing none. In all cases, optometrists referred stroke patients to other health care professionals more often than receiving incoming referrals from them. About 21% of all respondents were already fully involved in post-stroke vision care and 57% were interested in being more involved. Limiting factors to seeing more post-stroke patients included resources (29.8%), funding (25.8%), awareness (25.1%), and interest (9.8%). There was consensus among respondents in all countries that optometrists should be members of post-stroke care teams. CONCLUSION: Optometrists are well suited to provide post-stroke visual rehabilitation but are often not included in care teams for these patients. As a result, the visual management of post-stroke patients often is unaddressed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".