Bibliographic record
Abstract
Purpose: This case report describes the potential impact of tattooed eyeliner on eyelid structure and function resulting in an increase in dry eye symptoms and findings. Case Report: A 59-year-old Hispanic female presented for an evaluation following longstanding dry eye symptoms with little relief from artificial tears. Imaging showed meibomian gland dropout, possibly a result of her tattooed eyeliner. Symptoms and objective measurements improved successfully with warm compresses, lid massage, and lipid-based artificial tears. Conclusions. Permanent tattooed eyeliner may enhance dryness of the eyes in two main ways: disruption of the architecture of the lids and chronic inflammation from tattoo pigment granules. Recognizing these possible effects in patients with tattooed eyeliner may help tailor treatment to be specific to the etiology of the patient’s dry eye: aiding the remaining meibomian glands by utilizing warm compresses, lid massage and supplementing the lipid from the missing meibomian glands by employing lipid-based artificial tears.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".