Evaluation of Potential Distractors in the Urology Operating Room
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Surgical outcomes depend on patient and disease-related factors, as well as the technical skill of the surgeon. Various distractions in the operating room (OR) environment have been shown to negatively impact a surgeon's performance. A survey was conducted with the objective to evaluate and characterize distractions during urologic surgery. METHODS: An Internet-based survey was distributed to 2057 international urologists via email between April and October 2011; questions focused on a variety of disruptive factors postulated to have a negative impact on surgical performance. RESULTS: Of the 523 (25%) respondents, 58% practiced in North America, 42% were from an academic institution, and 68% had completed a clinical fellowship. In an average year, 83% reported having operated at least once while sleep deprived, 84% when significantly ill, 55% with a musculoskeletal injury, and 65% under significant social stress. Up to 38% reported that on at least one occasion, such "internal distractions" had significantly affected surgical performance and 14% perceived that at least one surgical complication was caused mainly by an internal distraction. Less than 50% had ever cancelled surgery because of an internal distraction. Music was routinely played in the OR by 57% of respondents, >67% reported answering pages and discussing consults while operating, and 25% reported "commonly" working with scrub nurses/techs that were unfamiliar with the procedure and/or instruments. Only 44% had consistent individual(s) assisting, and 27% reported that the scrub nurse/tech would "commonly" scrub out during a critical portion of the procedure. Overall, 14.5% reported that at least one complication had occurred mainly because of such "external" or "interactive" distractions. CONCLUSIONS: Urologists face various distractions in the OR that can negatively impact surgical performance, potentially compromising patient outcomes and safety. Further studies are needed to elucidate the true impact of such distractions and to develop strategies to mitigate their effects.
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 | 0.005 | 0.002 |
| 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 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".