Gait Analysis 1 Year after Primary TKA: No Difference between Gap Balancing and Measured Resection Technique
Bibliographic record
Abstract
Mechanical alignment in total knee arthroplasty (TKA) can be achieved using dependent bone cuts. The hypothesis is that patients have a better balanced TKA, as a result. The aim of this study was to determine if this technique is superior to an independent bone cut technique in terms of gait parameters, patient-reported outcome measures (PROMs), and satisfaction assessed before surgery and at 1-year follow-up. A total of 58 patients were evaluated before and 1 year following TKA, using the Press Fit Condylar (PFC) Sigma posterior stabilizer (PS) design; 39 (70 ± 8 years; 27 women) with independent bone cuts and 19 (71 ± 7 years; 12 women) with dependent bone cuts using the Specialist TRAM. Gait was evaluated with a three-dimensional motion analysis system for spatiotemporal and kinematics parameters. Pain and functional levels were assessed using the Western Ontario and McMaster Universities arthritis index (WOMAC); general health was assessed by the short form (SF)-12. Global satisfaction, as well as patient satisfaction, related to pain and functional levels were assessed using a five-point Likert's scale. No significant difference was found between both groups in terms of age, body mass index, pain, and functional levels at baseline. At 1-year follow-up, despite an overall improvement in gait, WOMAC, SF-12 physical score and pain, none of the patients showed gait parameters comparable to a healthy control group. No surgical technique effect was observed on gait, clinical outcomes, and satisfaction level. While observing an overall improvement at 1-year follow-up, we did not find any significant difference between the two surgical techniques in terms of gait parameters, patients' outcomes, and satisfaction.
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 | 0.000 | 0.001 |
| 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".