Intraoperative culture positive allograft bone and subsequent postoperative infections: a retrospective review
Bibliographic record
Abstract
BACKGROUND: Obtaining intraoperative cultures of allograft bone just before use in orthopedic procedures is standard practice in many centres; however, the association between positive cultures and subsequent surgical infections is unknown. Our study had 3 goals: to determine the prevalence of positive intraoperative allograft culture and subsequent infection; to determine if, in cases of subsequent infection, organisms isolated at reoperation were the same as those cultured from the allograft at the time of the index procedure; and to assess the costs associated with performing intraoperative allograft cultures. METHODS: In this retrospective case series, we obtained data on patients receiving allograft bone between 2009 and 2012. Patients receiving allograft with positive cultures were reviewed to identify cases of significant infection. Organisms isolated at reoperation were compared with the allograft culture taken at the time of implantation, and we performed a cost assessment. RESULTS: Of the 996 allograft bone grafts used, 43 (4.3%) had positive intraoperative cultures and significant postoperative infections developed in 2, requiring reoperation. Antibiotics based on culture results were prescribed in 24% of cases. Organisms cultured at the time of reoperation differed from those isolated initially. The cost of performing 996 allograft cultures was $169 320. CONCLUSION: This series suggests that rates of positive intraoperative bone allograft culture are low, and subsequent infection is rare. In cases of postoperative infection, primary allograft culture and secondary tissue cultures isolated different organisms. Costs associated with performing cultures are high. Eliminating initial culture testing could save $42 500 per year in our health region.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".