Deviations between intra-operative navigation data and post-operative weight-bearing X-rays
Bibliographic record
Abstract
Several studies have shown that computer-navigated TKA reduces the rate of outliers. Thirty-one consecutive patients were operated on by the same surgeon using a computer assisted navigation system. Data collected by the system included the final mechanical axis of the extremity (HKA angle) and the coronal angle of the tibial and femoral implants. These same values were measured using CAD software on full weight-bearing long X-rays taken 6 weeks post-surgery. Deviations were observed when X-ray measurements were compared to intra-operative data collected from the navigation system. A statistically significant difference was found in the tibial cut (1.29 degrees +/- 1.35 degrees; p < 0.0001) and in the HKA (1.59 degrees +/- 2.36 degrees; p = 0.0007). Outliers of more than 3 degrees were observed in the coronal plane of the tibial implant in 9.6% of patients, in the coronal plane of the femoral implant in 6.4% of patients, and in the HKA angle of 29% of patients. Our results indicate that the use of navigation alone is insufficient to prevent outliers beyond an acceptable range of 3 degrees .
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".