Patient‐Specific Instrumentation in Total Knee Arthroplasty
Bibliographic record
Abstract
This chapter presents a case scenario of a 65-year-old male patient with tricompartmental knee osteoarthritis. He is interested in total knee arthroplasty (TKA), and has heard about “personalized” implants on social media. Patient-specific instrumentation (PSI) has increased in popularity in recent years as orthopedic surgery responds to growing trend of personalized medicine. Multiple randomized controlled trials have compared PSI to standard instrumentation in terms of radiographic outcomes. Computer-assisted navigation and robotic-assisted total joint replacement surgery do appear to result in accurate component positioning. Patient-specific instrumentation has a theoretical potential to alleviate at least some of these concerns, such as component position, unnecessary bony resection, and soft tissue dissection. PSI is available in one of two main ways: through the company providing the TKA implants or through the hospital, in form of three-dimensional planning and printing of instruments, followed by on-site sterilization and packaging. The chapter provides recommendations for implementing evidence-based practice in the clinical setting.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".