A Comparative Study Between Patient-Specific Instrumentation and Conventional Technique in TKA
Bibliographic record
Abstract
Patient-specific instrumentation (PSI) was developed to improve the accuracy of component positioning through custom cutting blocks constructed based on preoperative 3-dimensional imaging in total knee arthroplasty (TKA). The purpose of this study was to compare the clinical and radiological outcomes between the patients who underwent PSI-assisted TKA or conventional TKA. Sixty-four patients (64 knees) underwent TKA by a single surgeon: 32 patients (32 knees) underwent TKA with PSI, 32 patients (32 knees) underwent TKA with conventional instrumentation. The mean age of the patients was 67.6 years, and the mean follow-up duration is 26.2 months. Patients were evaluated preoperatively and after surgery. The current authors evaluated clinical outcomes including knee range of motion, Hospital for Special Survey scale, Western Ontario and McMaster University Osteoarthritis Index, and Knee Society pain and function scores. The current authors also compared radiological outcomes including mechanical axis and coronal and sagittal alignment. The current authors found no significant differences in any clinical outcomes between the PSI-assisted TKA group and the conventional TKA group. In terms of radiological outcomes, the PSI-assisted TKA group had fewer alignment outliers. The current authors found that PSI-assisted TKA restores limb alignment better than conventional TKA, but PSI does not confer a substantial advantage in early functional outcomes after TKA. Further follow-up is needed to ascertain the long-term impact of these findings. [Orthopedics. 2016; 39(3):S83-S87.].
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".