Review of Eye Injuries Associated With Dermatologic Laser Treatment
Bibliographic record
Abstract
BACKGROUND: The eye is susceptible to damage during dermatologic laser treatments. OBJECTIVE: Discuss the anatomy of the eye related to these procedures, the principles of laser-eye interactions, and ocular injuries reported with dermatologic laser treatments. METHODS: PubMed and Embase searches were conducted to identify cases of eye injuries associated with dermatologic laser treatments. RESULTS: One hundred nineteen cases of eye injury associated with dermatologic laser treatments were identified. Fifty-nine cases targeted the eyelid during resurfacing and caused ectropion, while 60 cases resulted from direct injury of ocular structures. In most of the cases of the latter, improper eye protection was used (44 of 60, 73%). In nearly all these cases, it was the patient who sustained a potentially avoidable ocular injury (52 of 60, 87%). Thirty-one patients had persistent ocular symptoms at follow-up (52%). The most common procedure in this context was laser hair removal of the face (35 of 60, 58%). Most of the cases developed injuries specific for the particular laser based on its wavelength and affinity to target certain ocular chromophores (59 of 60, 98%). CONCLUSION: Most of the dermatologic laser-associated eye injury cases have occurred in the context of laser resurfacing or laser hair removal and are potentially preventable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".