INTRAOPERATIVE EVALUATION AND LEVEL OF CONTAMINATION DURING TOTAL KNEE ARTHROPLASTY
Bibliographic record
Abstract
ABSTRACT Introduction: Despite numerous articles on intraoperative contamination in total knee arthroplasty (TKA) procedures, the available data on tissue contamination are scarce and mainly based on evaluating bacteriological swabs. Methods: Two hundred and forty specimens, divided between bone and soft tissue, were obtained from 40 consecutive unilateral primaries TKAs. The specimens were evaluated for aerobic and anaerobic bacterial growth. Colony-forming units/gram (CFU/g) were calculated on the contaminated specimens to determine the level of contamination. Results: The contamination rate in intraoperative specimens was 15% during TKA. The contamination level had a mean of 10.6 and a median of 5, ranging from 1-70 CFU/g. The most common contaminating organisms in all samples were Staphylococcus aureus (38.9%) and Staphylococcus epidermidis (30.6%). No clinical infections were detected in TKAs in the follow-up period. Conclusion: The contamination rate during TKA is relatively high, despite the practice of standard preventive measures. Contamination levels, measured by CFU/g, are considered low when compared to the infection threshold of 105 reported in the literature. However, contamination should not be underestimated due to the presence of foreign bodies (implants), which are known to significantly lower this threshold. Level of evidence IV; case series .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".