Adverse Events Resulting from Lasers Used in Urology
Bibliographic record
Abstract
OBJECTIVE: To collate world reports of adverse events (AEs) resulting from lasers used in urology. METHODS: The Manufacturer and User Facility Device Experience (MAUDE) database of the United States Food and Drug Administration (FDA) was searched using the term "Laser for gastro-urology use." In addition, the Rockwell Laser Industries (RLI) Laser Accident Database was searched for the following types of lasers: neodymium-doped yttrium aluminum garnet (Nd:YAG), holmium:yttrium aluminum garnet (Ho:YAG), potassium titanyl phosphate (KTP), diode and thulium:YAG (Tm:YAG). RESULTS: Both databases were last accessed on October 1, 2012. Overall, there were 433 AEs; 166 in MAUDE database (1992-2012) and 267 in RLI database (1964-2005). Most of the AEs (198/433 or 46%) resulted from generator failure or fiber tip breaking. Whereas there were 20 (4.6%) AEs harming medical operators, there were 159 (37%) AEs harming nonmedical operators using Nd:YAG, KTP, and diode lasers. Eye injuries ranging from mild corneal abrasions to total vision loss were reported in 164 AEs with the use of Nd:YAG, KTP, and diode lasers. Overall, there were 36 (8.3%) AEs resulting in patient harm, including 7 (1.6%) mortalities, 3 deaths from ureteral perforation using the Ho:YAG laser, and 4 deaths from air emboli using the Nd:YAG laser. Other reported patient injuries included bladder perforation resulting in urinary diversion in a patient, in addition to minor skin burns, internal burns, and bleeding in others. There were no AEs reported with the use of Tm:YAG laser. CONCLUSIONS: Most of the AEs reported relate to equipment failure. There were no eye injuries reported with the use of Ho:YAG lasers. Caution must be exercised when using lasers in urology, including wearing appropriate eye protection when using Nd:YAG, KTP, and diode lasers.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".