A cross-sectional study of optometrists' attitudes towards dry eye disease management in Hong Kong: A web-based survey in Hong Kong
Bibliographic record
Abstract
Purpose: This study aimed to explore the practices of optometrists in Hong Kong towards diagnosing and managing dry eye disease (DED). Methods: From September 2021 to March 2022, an online questionnaire was distributed to optometrists in Hong Kong through several professional associations. The questionnaire included questions about the importance and usefulness of various diagnostic tests, as well as inquiries about management strategies and recommended follow-up schedules for DED. Responses were compared between optometrists who were more or less proactive in continuing education to identify potential differences. Results: The analysis included 68 valid responses. Sixty-one of them were Part 1 optometrists that represents 5.5 % of registered Part 1 optometrists back in 2022. Assessment of clinical symptoms was the most commonly performed investigation (93 %) and considered the most important (75 %) procedure in DED assessments, followed by corneal staining and fluorescein tear break-up time. Traditional diagnostic tests were preferred over newer methods, such as osmolarity, which were not yet commonly used. Unpreserved lubricants (90 %) and lid hygiene (63 %) were the primary treatments recommended for mild DED. Optometrists who had more experience and frequent participation in continuing education were more confident in diagnosing and managing DED, and more likely to recommend omega-3 supplements for moderate DED. Conclusion: The diagnostic and management strategies of optometrists in Hong Kong were generally consistent with the recommendations of the Dry Eye Workshop II report. However, standardized DED questionnaires and newer diagnostic tools were not commonly used. Evidence-based optometric care for dry eye management should be encouraged in Hong Kong optometric practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".