TFOS DEWS III: Digest
Bibliographic record
Abstract
This digest summarizes the interdisciplinary research in dry eye disease (DED) published since the 2017 TFOS DEWS II reports. It comprises 7 topics including Sex, Gender, and Hormones; Epidemiology; Pathophysiology; Tear Film; Pain and Sensation; Iatrogenic Dry Eye; and Clinical Trial Design and explores how each of these inform diagnostic methodology, disease subtype, and management of DED. Sex- and gender-related differences significantly influence the ocular surface due to hormones, sex chromosomes, sex-specific autosomal factors, epigenetics, care-seeking behaviors, and service use. Epidemiologic data reveal that DED prevalence varies by age and sex, influenced by diagnostic criteria and the multifactorial nature of the disease. New risk factors for DED include environmental, iatrogenicity, systemic diseases, and lifestyle domains. Pathophysiological distinctions between aqueous deficient and more evaporative forms of DED have been clarified, with the latter most commonly characterized by a muted inflammatory response at the ocular surface, meibomian gland dysfunction, and conceivably phenotypic changes in corneal epithelial cells. There is an expanding role for metabolic, hormonal, physical, neural and cellular stresses, including hyperosmolarity, mitochondrial stress, and neurogenic inflammation. Advancements in tear film research recommend new approaches to understanding DED pathogenesis and identifying biomarkers, such as microRNAs. Ocular pain perception is linked to structural integrity of corneal nerves, functional capacities of neurons, and activity of the central and peripheral nervous systems. Iatrogenic DED can result from medications, contact lenses, and surgical procedures. Clinical trials now emphasize aligning design and end points with DED subtypes and therapeutic mechanisms, with new therapeutics and trial designs under consideration.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.270 | 0.172 |
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".