Using 3-Tesla Magnetic Resonance Imaging as an Additional Radiographic Decision Aid for Oxford Unicompartmental Knee Arthroplasty in Patients without Bone-on-bone Lesion
Bibliographic record
Abstract
Background: The use of radiological decision aids for Oxford unicompartmental knee arthroplasty (OUKA) has a high false-negative rate. Objectives: The 3-tesla (3T) magnetic resonance imaging (MRI) system is more accurate for the decision of surgical indication for OUKA. Materials and Methods: Medical records were reviewed retrospectively for patients receiving OUKA. All patients had a preoperative 3T MRI scan, which identified full-thickness cartilage loss (FTCL). Evidence of bone-on-bone lesions from plain X-ray, bone marrow edema (BME), and medial meniscus root tear (MMRT) was also recorded. Clinical outcomes were assessed using the Oxford knee score (OKS), Tegner Lysholm knee scoring system (TLKSS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) 2 years postoperatively. Results: We reviewed 128 patients (140 knees, 44 in male patients, and 96 in female patients) with FTCL on 3T MRI. There were no significant differences in the TLKSS, OKS, or WOMAC between groups with and without bone-on-bone lesions, BME, or MMRT 2 years postsurgery. Conclusions: The 3T MRI system is an applicable radiographical decision aid for OUKA patient selection. FTCL on 3T MRI is sufficient for identifying OUKA beneficiaries, regardless of bone-on-bone lesions, BME, or MMRT Level of Evidence: Level III, retrospective comparative therapeutic trial.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".