Migration of Cosmetic Products into the Tear Film
Bibliographic record
Abstract
PURPOSE: To examine, record, and quantify the migration of a conventional eye cosmetic pencil when applied to periocular skin in two different locations: behind the lash line (ELI) and along the periocular skin (ELO). METHODS: This was a pilot study (prospective, randomized crossover design) involving two visits on separate days. Three female subjects were randomly assigned one of two eyeliner application conditions: ELI (inside the lash line) or ELO (anterior to the lash line). Pencil eyeliner ("Glimmerstick" in Graphite; Avon, Northampton, United Kingdom) was applied to the subject's upper and lower right eyelid by the examiner. Slitlamp video recording of glitter particles suspended within the tear film was conducted for 30 sec on 10 occasions up to 2 hr post-eyeliner application. The number of glitter particles suspended in the tear film, analyzed using ImageJ software, is reported. RESULTS: The migration of the glitter particles occurred more readily in ELI application, with maximum contamination of the tear film achieved 5 to 10 min post-application. The migration of eyeliner following ELO application was comparatively slower and reduced compared with ELI application. The quantity of glitter particles suspended in the tear film varied between subjects; however, 2 hr post-application, contamination of the tear film from pencil eyeliner was negligible. CONCLUSIONS: Pencil eyeliner migrates most readily and maximally contaminates the tear film when applied posterior to the lash line. This has implications for contact lens wearers and patients with dry eye syndrome or sensitive eyes. Eye cosmetic usage for participants involved in anterior eye and contact lens research should be carefully considered in the design of studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".