Computer navigation <i>versus</i> conventional total knee replacement
Bibliographic record
Abstract
We previously compared the component alignment in total knee replacement using a computer-navigated technique with a conventional jig-based method. We randomly allocated 71 patients to undergo either computer-navigated or conventional replacement. An improved alignment was seen in the computer-navigated group. The patients were then followed up post-operatively for two years, using the Knee Society score, the Short Form-36 health survey, the Western Ontario and McMaster Universities osteoarthritis index, the Bartlett Patellar pain questionnaire and the Oxford knee score, to assess functional outcome. At two years post-operatively 60 patients were available for assessment, 30 in each group and 62 patients completed a postal survey. No patient in either group had undergone revision. All variables were analysed for differences between the groups either by Student's t-test or the Mann-Whitney U test. Differences between the two groups did not reach significance for any of the outcome measures at any time point. At two years postoperatively, the frequency of mild to severe anterior pain was not significantly different (p = 0.818), varying between 44% (14) for the computer-navigated group, and 47% (14) for the conventionally-replaced group. The Bartlett Patellar score and the Oxford knee score were also not significantly different (t-test p = 0.161 and p = 0.607, respectively). The clinical outcome of the patients with a computer-navigated knee replacement appears to be no different to that of a more conventional jig-based technique at two years post-operatively, despite the better alignment achieved with computer-navigated surgery.
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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 | 0.002 |
| 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".