Management of Periprosthetic Joint Infections After Hemiarthroplasty of the Hip
Bibliographic record
Abstract
➢: Periprosthetic joint infection (PJI) following hip hemiarthroplasty (HA) is a devastating complication, incurring immense health-care costs associated with its treatment and placing considerable burden on patients and their families. These patients often require multiple surgical procedures, extended hospitalization, and prolonged antimicrobial therapy. ➢: Notable risk factors include older age, higher American Society of Anesthesiologists (ASA) score, inadequate antibiotic prophylaxis, non-antibiotic-loaded cementation of the femoral implant, longer duration of the surgical procedure, and postoperative drainage and hematoma. ➢: Although the most frequent infecting organisms are gram-positive cocci such as Staphylococcus aureus, there is a higher proportion of patients with gram-negative and polymicrobial infections after hip HA compared with patients who underwent total hip arthroplasty. ➢: Several surgical strategies exist. Regardless of the preferred surgical treatment, successful management of these infections requires a comprehensive surgical debridement focused on eradicating the biofilm followed by appropriate antibiotic therapy. ➢: A multidisciplinary approach led by surgeons familiar with PJI treatment and infectious disease specialists is recommended for all cases of PJI after hip HA to increase the likelihood of treatment success.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".