TFOS DEWS III: Management and Therapy
Bibliographic record
Abstract
This report provides an evidence-based review of current strategies to manage dry eye disease (DED). First-line management focuses on methods to replenish, conserve, and stimulate the tear film, with an emphasis on ocular supplements, which remain the cornerstone of DED treatment. Meibomian gland dysfunction, a primary contributor to DED, is typically treated with warm compresses and a wide variety of in-office treatments, including device-driven technologies to warm the eyelids, intense pulsed light therapy, low-level light therapy, and other new and emerging technologies. Lid hygiene treatments include lid wipes, anti-Demodex therapies, blepharoexfoliation, and topical antibiotics. DED caused by certain etiological drivers can benefit from anti-inflammatory therapies, including topical and oral corticosteroids, T-cell immunomodulatory drugs, and a wide variety of pharmacological agents, in addition to biologic tear substitutes such as autologous serum and platelet-rich plasma. Emerging therapies, such as neuromodulation via nasal neurostimulation and novel pharmacological treatments, offer potential future options. Advanced options, including amniotic membrane grafts and complex surgical methods, provide options for severe or refractory cases. Lifestyle modifications, including optimized blinking, dietary supplementation, and environmental adjustments, play a crucial role in long-term management. Patient education and adherence to treatment regimens remain essential for sustained symptom relief. The TFOS DEWS III prescribing algorithm provides an evidence-based framework to offer guidance to clinicians in selecting relevant interventions based on disease etiology that aim to provide targeted management of the relevant DED subtype(s) that an individual is experiencing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".