The Influence of Component Rotational Malalignment on Early Clinical Outcomes in Total Knee Arthroplasty
Bibliographic record
Abstract
Background The most important cause of patient dissatisfaction following total knee arthroplasty (TKA) is pain. Component rotation is an important factor in the clinical success of TKA. This study aims to determine component rotational errors in patients with mobile- and fixed-bearing polyethylene inserts after TKA and also to evaluate the effect of possible malrotations on clinical outcomes. Methods Seventy-five knees from sixty-six patients who underwent TKA were evaluated retrospectively. The patients were divided into two groups according to whether they received a mobile-bearing polyethylene insert (group 1, n = 48) or a fixed-bearing polyethylene insert (group 2, n = 27). The Hospital for Special Surgery (HSS) score, the Western Ontario and McMaster Universities Arthritis Index (WOMAC), the Lysholm Knee Scoring Scale, and the Oxford Knee Score were used for the clinical evaluation of the patients. The rotational state of the components was evaluated by computed tomography. Results The HSS, WOMAC, Lysholm, and Oxford clinical scores were not significant between the two groups (p > 0.05). The effect of femoral versus tibial component rotational deviation on clinical scores was not significant between the two groups (p > 0.05). Component rotational differences did not have a significant effect on the degree of knee flexion and extension between groups (p > 0.05). When the combined rotations of the components were compared with the clinical scores of function, no significant difference was detected between groups (p > 0.05). In addition, no significant difference between the operated sides of the patients and the combined component internal rotations was found (p > 0.05). Conclusion Although component rotation is an important factor in the clinical success of TKA, the current study did not find a clear association between the clinical results after TKA and the internal rotation of components. Component internal rotation alone is not an important predisposing factor for pain development after TKA. We believe that this may be attributed to the significant effects of patient expectation, which is often ignored, on clinical scores.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".