Joint line reestablishment in revision total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: In this study, the traditional "Anatomical Landmark-Distance Method (AL-DM)" in the formation of joint line (JL) was compared with "Adductor Tubercle-Ratios method" (AT-RM), and the effect of reestablishment of JL on clinical and functional outcomes were evaluated. MATERIALS AND METHODS: 16 revision total knee arthroplasties (rTKAs) were performed by using "AT-RM" (group 1) and 16 rTKA by using "AL-DM" (group 2) in our clinic between 2015 and 2018. The data were prospectively collected and a total of 32 knees of 31 patients were analyzed. At the final follow-up, knee functions were evaluated by using Knee Society Score (KSS) knee and function, Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores, Short Form-36 (SF-36) questionnaires and physical examinations. RESULTS: Postoperative flexion arc was higher in Group 1. KSS knee and function scores were better in group 1. In group1, JL was reestablished successfully in all revision rTKAs in terms of ATJL and the tibial tubercle TT-JL ratios. The improvement in KSS knee and function scores and WOMAC scores were also better in group 1. Measurements showed that the improvement in KSS scores increased as AT-JL and TT-JL distances approached the calculated values. CONCLUSION: "AT-RM" was shown to be superior to the traditional distance method in terms of JL reestablishment. Functional results and patient satisfaction increased when JL was reestablished.
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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 | 0.002 |
| 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".