Are Optometrists Prepared to Be Involved in Post-Stroke Rehabilitation?
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Stroke survivors often experience various visual consequences that impact their daily life and may benefit from visual interventions. However, some of these usually go unaddressed as optometrists are rarely included in the post-stroke care pathway. Yet, optometrists are interested in contributing to the care of these patients. This survey evaluated the readiness of optometrists in diagnosing and managing visual disorders specific to stroke survivors. METHODS: A questionnaire was developed by the researchers, pilot tested by 5 research optometrists and 15 community optometrists, and modified based on the feedback. Practicing optometrists were invited to complete the anonymous online survey through optometric organizations in Canada, the US, Hong Kong, India, and the UK. RESULTS: Most respondents displayed strong knowledge, but 61.6% indicated that enhancing their knowledge would be helpful. The majority (87%) agreed that stroke is related to an increased incidence of falls. Participants' knowledge regarding the natural history of post-stroke visual disorders was poorer. There were also inconsistencies regarding what optometrists considered ideal interventions and what they undertook in practice. More than 50% of respondents reported that the quality of published evidence on post-stroke visual consequences was low or nonexistent. CONCLUSIONS: Overall, survey respondents displayed sufficient knowledge. However, there are areas of uncertainty in their knowledge, which in many cases correspond to real gaps in the available evidence. There is a need to identify and remediate these gaps to enable optometrists to deliver quality optometric care as collaborative members of the post-stroke professional team, which would eventually improve the rehabilitation of stroke survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".