Clinical and Radiographic Outcomes of Inversed Restricted Kinematic Alignment Total Knee Arthroplasty by Asia Specific (Huang’s) Phenotypes, a Prospective Pilot Study
Bibliographic record
Abstract
Inverse restricted kinematic alignment (irKA) was modified from restricted kinematic alignment for total knee arthroplasty (TKA). This prospective single-center study aimed to evaluate the outcomes of irKA-TKA on all knee subtypes classified by Asia specific (Huang's) phenotypes. A total of 96 knees that underwent irKA-TKA at one hospital between January 2018 and June 2020 were included, with 15 knees classified in Type 1, nine in Type 2, 15 in Type 3, 47 in Type 4, and 10 in Type 5 by Huang's phenotypes. Outcomes were knee alignment measures and patient-reported satisfaction evaluated by the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and traditional Chinese version of the Forgotten Joint Score-12 (FJS-12). Follow-up was one year. Type 4 knee was most significantly corrected in all angles by irKA-TKA, followed by Type 2 and 3 knees. Type 5 and 1 knee were only significantly corrected in some angles. The correlation between FJS-12 and WOMAC was good at 6 months (Pearson correlation coefficient (r) = 0.74) and moderate at 6 weeks, 3 months, and 12 months during follow-up (r = 0.37~0.47). FJS-12 and WOMAC displayed comparable hip-knee-ankle angle cut-off value (4.71° vs. 6.20°), sensitivity (70.49% vs. 67.19%), specificity (84.00% vs. 85.71%), and Youden index (54.49% vs. 52.90%) in prediction of good prognosis. In conclusion, irKA-TKA corrects knee alignment in all knee types with increasing satisfaction for one-year follow-up. Knees with presurgical varus deformity are most recommended for irKA-TKA. Both presurgical scores of the traditional Chinese version of FJS-12 and WOMAC predict the prognosis of irKA-TKA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".