3-Tesla Magnetic Resonance Imaging as an Additional Radiographic Decision Aid for Oxford Unicompartmental Knee Arthroplasty Patient Selection
Bibliographic record
Abstract
Abstract Background: The indications for Oxford unicompartmental knee arthroplasty (UKA) have been investigated for decades. The use of radiological decision aids for Oxford UKA is widespread; however, recent evidence suggests that there is a high false-negative rate. The 3-Tesla (3T) magnetic resonance imaging (MRI) system is more accurate than the 1.5T MRI system and may perform even better with Stage III and IV osteoarthritis. Here, we investigated the relationship between MRI findings and patient-reported outcomes following Oxford UKA. Methods: Medical records were reviewed retrospectively for 94 patients (101 knees) receiving Oxford UKA. All patients had a preoperative 3T MRI scan, which identified full-thickness cartilage loss. Evidence of bone-on-bone lesions from plain X-ray, bone marrow edema, and medial meniscus root tear 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) six months postoperatively. Results: We reviewed 94 patients (101 knees, 30 in male patients and 71 in female patients) with full-thickness cartilage loss on 3T MRI. There were no significant differences in the TLKSS, OKS, or WOMAC between groups with and without bone-on-bone lesions, bone marrow edema, or medial meniscus root tear six months postsurgery. Conclusion: The 3T MRI system is an applicable radiographical decision aid for Oxford UKA patient selection. Full-thickness cartilage loss on 3T MRI is sufficient for identifying Oxford UKA beneficiaries, regardless of bone-on-bone lesions, bone marrow edema, or medial meniscus root tear
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".