Prophylactic antibiotic regimens in tumor surgery (PARITY) survey
Bibliographic record
Abstract
BACKGROUND: Deep infection following endoprosthetic limb reconstruction for sarcoma of the long bones is a devastating complication occurring in 15% of sarcoma patients. Optimizing infection protocols and conducting definitive surgical trials are critical to improving outcomes. In this study, the PARITY (Prophylactic Antibiotic Regimens in Tumor Surgery) investigators aimed to examine surgeon preferences in antibiotic prophylaxis and perceptions about current evidence, as well as to ascertain interest in resolving uncertainty in the evidence with clinical trials. METHODS: We used a cross-sectional survey to examine current practice in the prescription of prophylactic antibiotics in Musculoskeletal Tumor Surgery. The survey was approved by our institution's Ethics Board and emailed to all Active Members of the Musculoskeletal Tumor Society (MSTS) and Canadian Orthopaedic Oncology Society (CANOOS). Survey answers were collected using an anonymous online survey tool. RESULTS: Of the 96 surgeons who received the questionnaire, 72 responded (75% response rate (% CI: 65.5, 82.5%)). While almost all respondents agreed antibiotic regimens were important in reducing the risk of infection, respondents varied considerably in their choices of antibiotic regimens and dosages. Although 73% (95% CI: 61, 82%) of respondents prescribe a first generation cephalosporin, 25% favor additional coverage with an aminoglycoside and/or Vancomycin. Of those who prescribe a cephalosporin, 33% prescribe a dosage of one gram for all patients and the reminder prescribe up to 2 grams based on body weight. One in three surgeons (95% CI: 25, 48%) believes antibiotics could be discontinued after 24 hours but 40% (95% CI: 30, 53%) continue antibiotics until the suction drain is removed. Given the ongoing uncertainty in evidence to guide best practices, 90% (95% CI: 81, 95%) of respondents agreed that they would change their practice if a large randomized controlled trial showed clear benefit of an antibiotic drug regimen different from what they are currently using. Further support for a clinical trial was observed by an overwhelming surgeon interest (87%; 95% CI: 77, 93%) in participating in a multi-center randomized controlled study. CONCLUSION: The current lack of guidelines for the prescription of prophylactic antibiotics in Musculoskeletal Tumor Surgery has left Orthopaedic Oncologists with varying opinions and practices. The lack of current evidence and strong surgeon support for participating in a definitive study provides strong rationale for clinical trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".