Prevalence of Orthopedic Complaints Among Endourologists and Their Compliance with Radiation Safety Measures
Bibliographic record
Abstract
OBJECTIVES: To assess the compliance of endourologists with radiation safety measures and to determine the prevalence of orthopedic complaints among practicing endourologists. METHODS: An Internet-based survey was sent to all members of the Endourological Society. Baseline characteristics on practice patterns (geographical region, age, years of practice, days per week of endourology, and number of cases in the previous year), compliance with various radiation protection measures (thyroid, chest and pelvic shields, gloves, glasses, and dosimeters), and prevalence of various orthopedic complaints (neck, back, hand, and joint problems) were assessed. Furthermore, open-ended questions assessed reasons for noncompliance. RESULTS: Out of 160 surveys returned, 24 were excluded because of incomplete data. There was good compliance with chest and pelvic shields with 97% of endourologists reported wearing these. However, compliance with thyroid shields was only 68%. Furthermore, only 34.3%, 17.2%, and 9.7% of endourologists reported using dosimeters, lead-impregnated glasses, and gloves, respectively. Overall, 86 (64.2%) respondents complained of orthopedic problems. Specifically, 51 (38.1%) complained of back problems, 37 (27.6%) neck problems, 23 (17.2%) hand problems, and 19 (14.2%) complained of hip and knee problems. The prevalence of orthopedic complaints was significantly higher among African endourologists, older endourologists (>40 years), longer duration of practice (>10 years) and combined annual caseload of ureteroscopies (URS) and percutaneous nephrolithotomies (PCNL). CONCLUSIONS: Compliance in the use of thyroid shields, dosimeters, and lead-impregnated glasses and gloves could be improved. Orthopedic complaints among practicing endourologists are common and correlate with the annual caseload of combined URS and PCNL.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".