The Endourological Society Inaugural Census Report
Bibliographic record
Abstract
The Endourological Society, the premier urologic society encompassing endourology, robotics, and focal surgery, is composed of a diverse group of >1300 urologists. However, limited information has been collected about society members. Recognizing this need, a survey was initiated to capture data regarding current member practices, as well as help the Society shape the future direction of the organization. Presented herein is the inaugural Endourological Society census report as the beginning of a continued effort for global improvement in the field of endourology. Using a REDCap ® database, an email survey was circulated to the membership of the Endourological Society from May through June 2021. Twenty questions were posed, categorizing member data in terms of epidemiology/demographics, practice patterns, member opinions, and future educational preferences. Responses were received from 534 members, representing 40.3% of membership. Data demonstrated that the average age, gender, race, and ethnicity of the typical Society member respondent is a 48-year-old Caucasian male working in the United States, with a mean of 25 years in practice. Retrograde endoscopy and percutaneous nephrolithotomy were identified as the most common practice skills, and 50% of members are involved in robotics. Importantly, the census confirmed that the World Congress of Endourology and Technology remains popular with Society members as a means of educational advancement. To sustain and advance the Society, information is required to understand the career interests and future educational desires of its members. This inaugural census provides crucial data regarding its membership and how the Society can achieve continued success and adjust its focus. Future census efforts will expand on the initial findings and stratify the data to elucidate changes in the needs of the Society as a whole. Circulating an annual census will allow for continued improvements in the field of endourology and, ultimately, better care for urologic patients.
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 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".