[Gait analysis after total knee arthroplasty assisted by three-dimensional printing navigation template].
Bibliographic record
Abstract
OBJECTIVE: To explore the gait trajectory characteristics of patients after total knee arthroplasty (TKA) assisted by three-dimensional (3D) printing navigation template. METHODS: >0.05). The HKA, Western Ontario and McMaster University Osteoarthritis Index (WOMAC), and visual analogue scale (VAS) scores of the 3D printing group before and after operation were compared. At 6 months after operation, the gait trajectory characteristics of 3D printing group and control group were analyzed by Vicon gait capture system. The kinematics parameters included velocity, cadence, stride length, maximum knee flexion angle (stance), minimum knee flexion angle (stance), maximum knee flexion angle (swing), mean hip rotation angle (stance), mean ankle rotation angle (stance). RESULTS: <0.05). CONCLUSION: Assisted by 3D printing navigation template, TKA can alleviate pain symptoms and correct deformity, with satisfactory early effectiveness. Compared with healthy people, the early postoperative gait of the patients were characterized by decreasing velocity, cadence, stride length, knee flexion range, and increasing compensatory hip and ankle rotation range.
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".