CUAJ • May-June 2013 • Volume 7, Issue 5-6 © 2013 Canadian Urological Association
Bibliographic record
Abstract
Background: Radical cystectomy may result in significant blood loss necessitating transfusion. The purpose of this study was to determine what intra-operative techniques and hemostatic agents are currently used by uro-oncologists to prevent and control blood loss during radical cystectomy. Methods: In August 2011, members of the Society of Urologic Oncology (SUO) were solicited to complete an online survey. Residents, fellows and non-urologists were excluded. Canadian members received a personal email invitation. Respondents were asked to provide demographic information and opinions regarding blood loss and transfusion. Participants were also asked to report techniques used to reduce blood loss. Results: Of the 34 Canadian SUO members with registered email addresses, 27 (79%) completed the survey and met inclusion crite-ria as staff urologists who perform radical cystectomy. In addition, 52 non-Canadian SUO members were included in the analysis. Among all SUO respondents, a high proportion (73; 88%) reported using topical hemostatic agents during cystectomy. Thirty-six (46%) surgeons reported occasionally using procedural techniques and 9 (11%) using systemic hemostatic agents. Number of years since training was associated with decreased use of topical agents and increased use of procedural techniques (p < 0.01). Number of cystectomies per year was associated with decreased use of topical hemostatic agents (p < 0.01). Interpretation: Based on a survey of practice, there is significant risk of blood loss requiring transfusion during radical cystectomy. Surgeons frequently use topical hemostatic agents and rarely use systemic drugs to prevent or control blood loss. Trials evaluating agents and techniques to reduce blood loss during radical cystec-tomy are needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.085 | 0.008 |
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".