Determining the use of prophylactic antibiotics in breast cancer surgeries: a survey of practice
Bibliographic record
Abstract
BACKGROUND: Prophylactic antibiotics (PAs) are beneficial to breast cancer patients undergoing surgery because they prevent surgical site infection (SSI), but limited information regarding their use has been published. This study aims to determine the use of PAs prior to breast cancer surgery amongst breast surgeons in Colombia. METHODS: An online survey was distributed amongst the breast surgeon members of the Colombian Association of Mastology, the only breast surgery society of Colombia. The scope of the questions included demographics, clinical practice characteristics, PA prescription characteristics, and the use of PAs in common breast surgical procedures. RESULTS: The survey was distributed amongst eighty-eight breast surgeons of whom forty-seven responded (response rate: 53.4%). Forty surgeons (85.1%) reported using PAs prior to surgery of which >60% used PAs during mastectomy, axillary lymph node dissection, and/or breast reconstruction. Surgeons reported they targeted the use of PAs in cases in which patients had any of the following SSI risk factors: diabetes mellitus, drains in situ, obesity, and neoadjuvant therapy. The distribution of the self-reported PA dosing regimens was as follows: single pre-operative fixed-dose (27.7%), single preoperative dose followed by a second dose if the surgery was prolonged (44.7%), single preoperative dose followed by one or more postoperative doses for >24 hours (10.6%), and single preoperative weight-adjusted dose (2.1%). CONCLUSION: Although this group of breast surgeons is aware of the importance of PAs in breast cancer surgery there is a discrepancy in how they use it, specifically with regards to prescription and timeliness of drug administration. Our findings call for targeted quality-improvement initiatives, such as standardized national guidelines, which can provide sufficient evidence for all stakeholders and therefore facilitate best practice medicine for breast cancer surgery.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".