Preliminary clinical effects of total knee arthroplasty with iASSIST navigation system
Bibliographic record
Abstract
Objective To explore the application value of iASSIST portable navigation in total knee arthroplasty. Methods Seventy-four patients with knee osteoarthritis from April 2016 to April 2017 were retrospectively recruited. Thirty-seven patients (37 knees) underwent TKA with iASSIST navigation, while 37 patients (37 knees) underwent conventional TKA. Five parameters were measured on the weight-bearing radiographs at six months after TKA, including mechanical axis (MA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), sagittal femoral component angle (sFCA) and sagittal tibial component angle (sTCA). Duration of operation, blood loss volume, postoperative hospital day, Western Ontario and McMaster Universities (WOMAC) osteoarthritis index, Knee Society Score (KSS) clinical score and functional score at 6 weeks, 12 weeks and 24 weeks after surgery were also recorded. Results The accuracy of MA (180.85°±0.88° versus 182.23°±1.09° in the conventional group, P 0.05). Conclusion More accurate restoration in mechanical axis and optimal implantation can be achieved with the help of iASSIST navigation. This navigation system can also achieve better knee function in the early stage after TKA. Key words: Osteoarthritis, knee; Arthroplasty, replacement, knee; Surgery, computer-assisted; X-rays
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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 | 0.003 |
| 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".