Delphi Panel Consensus Regarding Current Clinical Practice Management Options for Demodex blepharitis
Bibliographic record
Abstract
Purpose: blepharitis (DB) treatment using a modified Delphi panel process. Methods: Expert Panel on Treatment and Eyelid Health (DEPTH). They completed a live roundtable discussion in addition to 3 surveys consisting of scaled, open-ended, true/false, and multiple-choice questions pertaining to the treatment of DB. Consensus for scaled questions using a 1 to 9 Likert scale was predefined as median scores of 7-9 and 1-3. For other question types, consensus was achieved when 8 of 12 panelists agreed. Results: The experts agreed that an effective therapeutic agent for treatment of DB would likely decrease the necessity of mechanical intervention, such as lid scrubs or blepharoexfoliation (Median = 8.5; Range 2-9). When treating DB, panelists believed that collarettes serve as a surrogate for mites, and that eliminating or reducing collarettes should be the main clinical goal of treatment (Median = 8; Range 7-9). The panelists would treat patients with at least 10 collarettes, regardless of other signs or symptoms and agreed that DB can be cured, but there is always the possibility for a reinfestation (n = 12). There was also consensus that collarettes, and therefore mites, are the primary treatment target and the way by which clinicians can monitor patient response to therapy (Median = 8; Range 7-9). Conclusion: Expert panelists achieved consensus on key facets of DB treatment. Specifically, there was consensus that collarettes are pathognomonic for DB, that DB patients with >10 collarettes should be treated even in the absence of symptoms, and that treatment efficacy can be tracked by collarette resolution. By increasing awareness about DB, understanding the goals of and monitoring treatment efficacy, patients will receive better care and, ultimately, better clinical outcomes.
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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 | 0.237 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".