Lysine Analogue Use during Cancer Surgery: A Survey from a Canadian Tertiary Care Centre
Bibliographic record
Abstract
Background: When used during surgery, antifibrinolytic hemostatic agents such as lysine analogues are effective at reducing blood loss and the need for transfusions. Despite proven efficacy, use of hemostatic agents remains low during some surgeries. Our objective was to explore surgeon opinions about, and use of lysine analogues in, oncologic surgeries at a large tertiary care academic institution. Methods: We administered a survey to surgeons who perform high-transfusion-risk oncologic surgeries at a large academic hospital in Ottawa, Ontario. Design and distribution of the survey followed a modified Dillman method. To ensure that the survey questionnaire was relevant, clear, and concise, we performed informant interviews, cognitive interviews, and pilot-testing. The final survey consisted of 19 questions divided into 3 sections: respondent demographics, use of hemostatic agents, and potential clinical trial opinions. Results: Of 28 surgeons, 24 (86%) participated. When asked to indicate the frequency of lysine analogue use, "never" accounted for 46% of the responses, and "rarely" (<10% of the time) accounted for 23% of the responses. Reasons for never using included "unfamiliar with benefits" and "prefer alternatives." Fifteen surgeons (63%) felt that a trial was needed to demonstrate the efficacy and safety of lysine analogues in their cancer field. Conclusions: Our survey found that lysine analogues are infrequently used during oncologic surgeries at our institution. Many surgeons are unfamiliar with the benefits and side effects of lysine analogues and, alternatively, use topical hemostatic agents. Our results demonstrate that future trials exploring the efficacy and safety of lysine analogues in oncologic surgery 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".