Partial lateral patellar facetectomy combined with lateral retinaculum release for treatment of patellofemoral osteoarthritis
Bibliographic record
Abstract
Objective To evaluate the clinical outcomes of partial lateral patellar facetectomy (PLPF) combined with lateral retinaculum release (LRR) for treatment of patellofemoral osteoarthritis (PFOA). Methods From June 2017 to March 2018, 30 PFOA patients underwent PLPF combined with LRR at Department of Orthopedics and Traumatology, Foshan Hospital of Traditional Chinese Medicine. They were 7 men and 23 women with an average age of 56.4±9.7 years. Their patellar position, patellofemoral joint function, overall knee function, and quality of life were assessed by comparing preoperation and last follow-up in patellofemoral congruence angle (PFCA), lateral patellofemoral angle (LPFA), modified Kujala score, The Western Ontario and Mcmaster Universities Osteoarthritis Index (WOMAC), and SF-12 quality of life scale. Results All the patients were followed up for an average of 7.6±3.4 months (from 4 to 13 months). The PFCA was improved from preoperative 22.9°±7.6° to 12.4°±4.2° at the last follow-up, the LPFA from preoperative 3.2°±3.7° to 12.9°±6.0° at the last follow-up, the modified Kujala score from preoperative 17.1±9.8 to 34.3±5.7 at the last follow-up, the WOMAC from preoperative 14.1±5.2 to 5.9±1.7 at the last follow-up, the stiffness index from preoperative 5.5±3.2 to 2.7±1.2 at the last follow-up, daily functional index from preoperative 43.9±9.0 to 25.2±5.4 at the last follow-up, and the SF-12 scores from preoperative 31.3±5.2 to 55.7±6.0 at the last follow-up. All the above comparisons showed a significant difference (P<0.05). Conclusion PLPF combined with LRR is a minimally invasive, easy-to-master and effective knee joint preserving procedure for PFOA as it can significantly relieve joint pain and maximally keep patellar functions. Key words: Osteoarthritis, knee; Arthroplasty; Surgical procedures, minimally invasive; Lateral retinaculum release; Knee joint preserving procedure
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".