Does Patellectomy Jeopardize Function After TKA?
Bibliographic record
Abstract
BACKGROUND: The patella provides important mechanical leverage to the knee extensor mechanism. Patellectomy does not exclude the development of tibiofemoral arthrosis. QUESTIONS/PURPOSES: We asked whether (1) TKA provides improvements in clinical outcome scores in patellectomized knees and (2) the scores of TKA in patellectomized knees are comparable to those in knees with intact patellae. METHODS: We evaluated 50 patients (52 primary TKAs) with patellectomized knees and a control group of 52 patients (52 primary TKAs) with intact patellae matched for age, sex, implant, and surgical year between 1984 and 2009. We compared the preoperative and latest postoperative SF-12, WOMAC, and Knee Society score (KSS). Minimum followup was 24 months (mean, 69 months; range, 24-204 months). RESULTS: The mean WOMAC score in the control group improved from 41.8 (range, 7.5-72.4) preoperatively to 69.1 (range, 17.0-100.0) postoperatively, while that in the patellectomized group improved from 35.8 (range, 5.2-62.2) to 61.3 (range, 17.5-96.2). The mean KSS improved from 80.4 (range, 4.0-143.0) preoperatively to 161.4 (range, 69.0-200.0) postoperatively in the control group and from 76.9 (range, 5-134) to 136.8 (range, 7-199) in the patellectomized group. Mean postoperative WOMAC scores were comparable between the two groups, while the mean KSS was lower in the patellectomized group. The mean SF-12 scores were not different after TKA or between groups. CONCLUSIONS: Despite the mechanical disadvantage to the knee extensor mechanism rendered by a previous patellectomy, TKA for tibiofemoral arthrosis in these patients relieved pain and restored function, but function was on average lower than that in patients with intact patellae. LEVEL OF EVIDENCE: Level III, therapeutic study. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".