Computer-navigated versus conventional total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: The literature lacks studies that confirm whether the improved radiographic alignment that can be achieved with computer-navigated total knee arthroplasty (TKA) improves patients' activities of daily living or the durability of total knee prostheses. Thus, in this protocol, we designed a randomized controlled trial to compare implant alignment, functional scores, and survival of the implant using computer-assisted surgery versus a conventional surgical technique. METHODS: This prospective, blinded randomized controlled trial was conducted at our single hospital. This study was approved by the ethics committee of Jiaxing Second Hospital. The patient inclusion criteria were age 20 to 80 years' old, a body mass index of ≤35 kg/m, and consented for primary knee arthroplasty performed through a medial parapatellar approach by the senior author. We randomized consented study participants on a 1:1 ratio to 1 of 2 study groups using a computer-generated list of random numbers in varying block sizes. The primary outcome in this study was the Knee Injury and Osteoarthritis Outcome Score. Secondary outcomes were the Knee Society Score, Western Ontario and McMaster Universities Osteoarthritis Index, complications, and range of motion together with alignment and rotational positioning of the implant. Statistical significance was defined as a P value of ≤0.05. CONCLUSIONS: Authors hypothesized that computer-assisted surgery in primary TKA improves implant alignment, functional scores, and survival of the implant compared to the conventional technique.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".