Adjuvant antibiotic‐loaded bone cement: Concerns with current use and research to make it work
Bibliographic record
Abstract
Antibiotic-loaded bone cement (ALBC) is broadly used to treat orthopaedic infections based on the rationale that high-dose local delivery is essential to eradicate biofilm-associated bacteria. However, ALBC formulations are empirically based on drug susceptibility from routine laboratory testing, which is known to have limited clinical relevance for biofilms. There are also dosing concerns with nonstandardized, surgeon-directed, hand-mixed formulations, which have unknown release kinetics. On the basis of our knowledge of in vivo biofilms, pathogen virulence, safety issues with nonstandardized ALBC formulations, and questions about the cost-effectiveness of ALBC, there is a need to evaluate the evidence for this clinical practice. To this end, thought leaders in the field of musculoskeletal infection (MSKI) met on 1 August 2019 to review and debate published and anecdotal information, which highlighted four major concerns about current ALBC use: (a) substantial lack of level 1 evidence to demonstrate efficacy; (b) ALBC formulations become subtherapeutic following early release, which risks induction of antibiotic resistance, and exacerbated infection from microbial colonization of the carrier; (c) the absence of standardized formulation protocols, and Food and Drug Administration-approved high-dose ALBC products to use following resection in MSKI treatment; and (d) absence of a validated assay to determine the minimum biofilm eradication concentration to predict ALBC efficacy against patient specific micro-organisms. Here, we describe these concerns in detail, and propose areas in need of research.
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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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".