Computer navigated total knee arthroplasty: Aspects of a single unit's experience of 777 cases
Bibliographic record
Abstract
The use of computer navigation and conventional techniques in total knee arthroplasty remains controversial. Advocates of computer navigated techniques cite better alignment of components and reduced morbidity associated with avoidance of intramedullary instrumentation as a rationale for their use. In contrast, proponents of conventional techniques argue that better alignment does not correlate with a better functional outcome and that the conventional approach avoids the perceived risk of fracture associated with bicortical insertion of navigation tracker pins. All total knee arthoplasties performed at our institution are prospectively monitored for life in a dedicated Joint Replacement Assessment Clinic (JRAC). Patients are reviewed by physiotherapists, independent of the surgeons who performed surgery, both preoperatively and at six weeks, three and six months, and one, two and five years postoperatively (and every five years thereafter). Patients are assessed using validated outcome measures (Knee Society Score, Western Ontario and McMaster Universities (WOMAC) osteoarthritis index, Short Form SF-36 Health Survey (version 2) and a patient satisfaction score). In addition, at 6 months post surgery, a CT scan of each implanted prosthesis is performed using the Perth CT knee protocol. The findings of a single unit's experience of 777 navigated primary total knee replacements are discussed and critically compared to the body of literature that currently relates to this controversial topic.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".