Clinical practice patterns in the management of dry eye disease: A TFOS international survey 2023-24
Bibliographic record
Abstract
AIMS: To understand current clinical management of dry eye disease (DED), based on its perceived severity and subtype, by practitioners across the world. METHODS: The content of the anonymous survey was chosen to reflect the DED management strategies reported by the Tear Film and Ocular Surface Society (TFOS) second Dry Eye Workshop (DEWS II). Questions were designed to ascertain practitioner treatment choice, depending on the subtype and severity of DED. It was first created in English and then translated/back-translated into 14 languages for online completion. RESULTS: Completed surveys were received from 905 eye care practitioners (52 % optometrists and 42 % ophthalmologists) from across the globe. Many treatment strategies for DED were observed to be utilised by respondents, independent of severity and subtype, the most common being advice (82 %), low (82 %) and high (81 %) viscosity unpreserved lubricants and lid wipes/scrubs (79 %). Several treatments were prescribed across all severity levels (scaled from 1 mild to 10 severe), such as advice (median 4.5, range 4.8), artificial tears (median 5.1, range 4.6) and nutritional supplements (median 5.3, range 4.2). Others were prescribed more frequently with increasing disease severity, for instance, biologics (median 8.2, range 2.8) and surgical approaches (median 8.1, range 2.2). While a similar number of practitioners reported prescribing advice, artificial tears and anti-inflammatories regardless of DED subtype, the commonly reported approaches for aqueous deficient DED were punctal occlusion, therapeutic contact lenses and secretagogues, while the use of oral essential fatty acids, topical lipid-containing products, lid hygiene and lid warming were the preferred management choices for evaporative DED. CONCLUSIONS: There remains great variability in clinical approaches to DED management and until research-evidence definitively informs improved guidance, data from this survey may be useful for clinicians to benchmark their practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".