Better safe than sorry? Results from an ex-vivo study demonstrate that the thulium fiber laser may cause eye injury without standard protection
Bibliographic record
Abstract
INTRODUCTION: We conducted a study using an ex-vivo porcine model to evaluate whether a thulium fiber laser (TFL) induces ocular injury in the context of inadvertent exposure to the laser beam. METHODS: A 365 μm TFL was positioned at a set distance (0 cm, 5 cm, 8 cm, and 10 cm) from a freshly harvested (<12 hours) porcine eyeball and the laser was activated for one second at select laser settings for lithotripsy (0.2 J at 50 Hz, 0.5 J at 20 Hz, and 1 J at 10 Hz) and soft tissue ablation (2 J at 10 Hz, 1 J at 50 Hz). The experiment was repeated with laser safety goggles and prescription eyeglasses. Thermal injury was assessed by histopathological analysis. RESULTS: Without eye protection, corneal injury was observed even at 10 cm away for one lithotripsy setting (1 J at 10 Hz) and both tissue ablation settings. All thermal injuries observed were superficial only, except for at 0 cm distance, where deep-layer injury was observed. Laser safety goggles offered complete protection regardless of setting or distance. Partial protection was demonstrated with prescription glasses: histopathological damage was observed for both soft tissue ablation settings and only at 0 cm for two lithotripsy settings (0.5 J at 20 Hz, 1 J at 10 Hz). CONCLUSIONS: The TFL can induce ocular injury at close distances and at higher power settings. The use of laser safety goggles confers complete protection while prescription eyeglasses confer partial protection. Further study is warranted.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".