Preventing Eye Injuries From Light and Laser-Based Dermatologic Procedures: A Practical Review
Bibliographic record
Abstract
The light and laser-based devices used in cosmetic dermatology practice carry a risk of serious ocular injury if appropriate safety measures are not put in place. Currently there is a lack of enforced regulation around the use of these devices. This raises concerns for the handling of these devices by operators who may not have adequate training and qualifications. There is also no mandated reporting of adverse events, thus precluding determination of the true incidence of laser-induced ocular injuries. To decrease the risk of ocular and periocular laser-induced injuries, several practical measures can be implemented within the clinical setting. Scientific articles were identified by performing a literature review using terms relevant to laser eye safety and a narrative review was performed. This article explores several components of laser eye safety: patient screening and informed consent, clinical environment considerations, operator considerations, protective eyewear selection for operators and patients, when to use a corneal shield, how to place a corneal shield and what to do in the event of a suspected eye injury. It is our prerogative that a functional understanding of the scientific underpinnings of laser eye safety coupled with observance of published standards has the potential to reduce incidents.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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".