Meniscal Repair and Parameniscal Cyst Excision with Knee Arthroscopic Surgery: A Case Report
Bibliographic record
Abstract
Background: Meniscal cysts were rare case. There’s a 50 to 100% chance forming cyst to the tear if there was an injury happens. Trauma would cause tears happen in meniscus, leads to formation of hemorrhage which formed mucoid degeneration. The necrosis happens locally plus degeneration of mucoid forming a cyst. Thus, the meniscal cysts challenge the clinician to have clinical reasoning so then the patient can get accurate diagnosis and preferred management. Method: This paper is a case report of surgery on a patient present with cysts on lateral para meniscus which done arthroscopically with all inside technique. Results: The procedure which done by arthroscope and motorized shaver had a great outcome in this patient which were analyze from Visual Analog Scale (VAS) and Western Ontario and McMaster Index (WOMAC) 3 months after the procedure, and reach VAS score of 2/10 which was mild pain and 59,8 in WOMAC score. Conclusion: The arthroscopy and all inside technique with motorized shaver is a choice of lateral para meniscal cysts surgery with good results to be considered by orthopedic surgeons. Key words: Lateral, Meniscus Tear, Parameniscal cyst, Arthroscopy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".