Routine use of antibiotic‐laden bone cement in total knee arthroplasty is a cost‐effective practice in the single‐payer healthcare system
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine the cost-effectiveness of antibiotic-laden bone cement (ALBC) in primary total knee arthroplasty (TKA) from the perspective of a single-payer healthcare system. METHODS: A cost-utility analysis (CUA) was performed over a 2-year time horizon comparing primary TKA with either ALBC or regular bone cement (RBC) from the perspective of the single-payer Canadian healthcare system. All costs were in 2020 Canadian dollars. Health utilities were in the form of quality-adjusted life years (QALYs). Model inputs for cost, utilities and probabilities were derived from the literature as well as regional and national databases. One-way deterministic sensitivity analysis was performed. RESULTS: Primary TKA with ALBC was found to be more cost-effective compared to primary TKA with RBC with an incremental cost-effectiveness ratio (ICER) of -3,637.79 CAD/QALY. The use of routine ALBC remained cost-effective even with cost increases of up to 50% per bag of ALBC. TKA with ALBC was no longer cost-effective if the rate of PJI following this practice increased 52%, or the rate of PJI following the use of RBC decreased 27%. CONCLUSIONS: The routine use of ALBC in TKA is a cost-effective practice in the single-payer Canadian healthcare system. This remains to be the case even with a 50% increase in the cost of ALBC. Policy makers and hospital administrators of single-payer healthcare systems can leverage this model to inform their local funding policies. Future prospective reviews and randomized controlled trials from the perspective of various healthcare models can further shed light on this issue. LEVEL OF EVIDENCE: III.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".