Antibiotic Cement Spacers for Infected Total Knee Arthroplasties
Bibliographic record
Abstract
Periprosthetic infection remains a frequent complication after total knee arthroplasty. The most common treatment is a two-stage procedure involving removal of all implants and cement, thorough débridement, insertion of some type of antibiotic spacer, and a course of antibiotic therapy of varying lengths. After some interval, and presumed eradication of the infection, new arthroplasty components are implanted in the second procedure. These knee spacers may be static or mobile spacers, with the latter presumably providing improved function for the patient and greater ease of surgical reimplantation. Numerous types of antibiotic cement spacers are available, including premolded cement components, surgical molds for intraoperative spacer fabrication, and the use of new metal and polyethylene knee components; all these are implanted with surgeon-prepared high-dose antibiotic cement. As there are advantages and disadvantages of both static and the various mobile spacers, surgeons should be familiar with several techniques. There is inconclusive data on the superiority of any antibiotic spacer. Both mechanical complications and postoperative renal failure may be associated with high-dose antibiotic cement spacers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".