Development and evaluation of a minimally aggressive method of patellofemoral osteoarthritis surgical treatment
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to assess the impact of developed minimally invasive operation such as the partial lateral facetectomy (PLFE) with patella thickness resection in patients with lateral patellofemoral (PF) osteoarthritis on short-term clinical and radiographic outcomes. METHODS: This is a prospective study of 27 knees in 27 patients (18 females and 9 males, mean aged at surgery 59.1-year old) whom a PLFE with patella thickness resection using developed minimally invasive method was performed. Data of preoperative and postoperative questionnaires, physical examinations, and radiographs were analyzed. The minimum follow-up was 12 months (mean, 24.1 months; range, 12-36 months). RESULTS: The subjective outcomes included the anterior pain relief assessed by scores using the Western Ontario and McMaster Universities Arthritis Index score (scores improved considerably by 2.34 points with respect to pain and by 1.63 points with respect to function), visual analog scale, and Knee Society Score (that improved in 78% of the knees). The majority of these patients experienced improvement in their PF symptoms. The PF index decreased considerably after surgery. The mean patellar width and thickness were decreased after surgery, maintaining a width/thickness ratio of 1.8:1.0 after surgery. CONCLUSION: PLFE with patella thickness resection aiming to decrease the high pressure in the lateral facet and improving congruence of the patella confirmed frequent pain relief. This surgical procedure is minimally invasive, relatively simple, and effective in selected patients and can be a valid early alternative to more complex operations. Level of Evidence: Level IV Therapeutic study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".