Minimizing Infection Risk: Fortune Favors the Prepared Mind
Bibliographic record
Abstract
Despite advances in care, infection in total joint arthroplasty remains a serious problem that has yet to be solved. Reported infection rates range from <0.5% in highly specialized centers to a high of 2% as reported at a national level. The epidemiology of total joint arthroplasty remains challenging because of the relatively low, but significant, incidence of infection. Still, there are variables that can be addressed that have demonstrated evidence regarding reduction in infection rates. These variables include optimizing medical conditions in the preoperative period such as anemia, blood glucose, and nutrition. In the perioperative period, administration of parenteral antibiotics within 1 hour of incision is a must. The effect of the operating room environment is less clear, but it is evident that traffic flow in the operating room has a negative effect on infection rates. Skin preparation with chlohexidine is now the agent of choice, and evidence exists that iodophor impregnated occlusive dressings add value. Razors should not be used. Surgical staples for closure have an increased risk of superficial infection as compared to subcuticular sutures. In the postoperative period, early, persistent wound drainage should be managed aggressively. There is no evidence to support the use of parenteral antibiotics past 24 hours in routine cases. Patients should be advised about prophylaxis for infection when undergoing dental work and other high-risk procedures. There is a strong movement to extend this prophylactic period indefinitely, as opposed to 2 years postoperatively. Finally, and perhaps most importantly, it is the surgeon's responsibility to be aware of all these issues and to strongly advocate for patient safety in ensuring that infection risk is minimized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".