Double-Blind Comparison of Eyelash Epilation Techniques for the Purpose of Demodex folliculorum Mite Retrieval
Bibliographic record
Abstract
OBJECTIVE: Demodex folliculorum blepharitis is typically confirmed with lash epilation and microscopic identification of mites. However, mite counts may vary with the epilation technique. As there is no gold standard to epilating lashes for the purposes of mite counts, the aim of this study was to compare three epilation techniques. METHOD: A prospective randomized double-blind study compared three epilation techniques on lashes with cylindrical dandruff. Techniques included (A) direct pulling of the lash; (B) rotating the lash before epilation; and (C) sliding the cylindrical dandruff away, lash rotation, and epilation. Mean mite counts were analyzed using a repeated-measures analysis of variance. RESULTS: Forty (n=40) participants (20 M: 20 F, mean age of 62.3±17.1 years) revealed similar mite counts between right (1.43±1.74) and left (1.35±1.59) eyes ( P =0.63). A significant difference ( P =0.03) in mite count was noted (technique A: 1.05 ± 1.60; technique B 1.76 ± 1.80; and technique C 1.36 ± 1.54) with technique B yielding the highest mite count ( P =0.04). CONCLUSION: Demodex mite count is a key parameter in establishing infestation or to determine treatment efficacy. This study revealed that rotating the lash before epilation yielded the highest mite count. Future studies should report the epilation technique used to allow for study comparisons.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".