Delphi panel on neuromodulation as a treatment strategy for dry eye disease: Unlocking the potential of natural tear production
Bibliographic record
Abstract
PURPOSE: Chronic tear deficiency, through reduced production and/or increased evaporation, is regarded as a root cause of dry eye disease (DED). The goal of treating DED is restoration of the tear film ultimately resulting in ocular surface homeostasis. Multiple therapeutic prescription drugs to manage DED exist with varying speed of onset, overall magnitude of efficacy, and tolerability. Neuromodulation is an emerging treatment modality offering direct stimulation of natural tear production. A modified Delphi study was conducted to explore the role of neuromodulation as a treatment for DED. METHODS: Twenty DED experts participated in three rounds of structured electronic Delphi questionnaires. Consensus, defined as ≥ 80 %, was sought on 18 statements across three key DED topics: unmet treatment needs, the importance of natural tears in ocular surface homeostasis, and neuromodulation as a treatment approach. Statements were refined iteratively based on qualitative feedback and quantitative agreement from the panel. RESULTS: Consensus was reached on all 18 statements. Panelists affirmed that significant unmet needs persist in managing DED. Panelists agreed that stimulating patients' natural tear production can help maintain and restore ocular surface homeostasis and that neuromodulation, through the ability to rapidly increase natural tear production, has the potential to effectively fill existing treatment gaps. CONCLUSION: This Delphi panel reached consensus on the importance of restoring natural tear production as a primary goal in treating DED. Neuromodulation represents a promising treatment option for DED, offering a rapid and restorative therapeutic approach for natural tear production.
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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 | 0.051 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".