Adverse Events Resulting from Lasers Used in Urology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it