Bibliographic record
Abstract
While no one would argue the necessary role for the medical management of patients with early knee arthritis, significant controversy remains regarding the ideal treatment for a patient with bone-on-bone osteoarthritis who could equally be treated with a high tibial osteotomy, a uni-compartmental total knee, potentially a patello-femoral replacement if dealing with isolated patello-femoral disease or lastly, a complete total knee replacement. While clearly to date there has not been consensus on this issue, a review of the arguments, both pro and con, should be used as a guide to the surgeon in making this clinical judgment. Patient Satisfaction: Many ardent supporters of uni-compartmental knee replacements espouse one of the principle benefits of the uni knee as much greater patient satisfaction. Unfortunately, what is never taken into account is the pre-selection bias that occurs in this patient population. Patients with the most minimal amount of arthritis and those with the greatest range of motion are pre-selected to undergo a uni-compartmental knee replacement compared to the more advanced arthritic knee with mal-alignment and more significant preoperative disability that will undergo a total knee replacement. Additionally the sources of data to draw the conclusions must be carefully analyzed. We must avoid using data from small series with unblinded patients performed by surgeons expert in the technique. Instead registry data, with its broad based applicability, is a much more logical source of information. Of significance, when over 27,000 patients were assessed regarding satisfaction following knee surgery; there was no difference in proportions of satisfied patients whether they had a total knee or a uni-compartmental knee. Implant Longevity: Once again large prospective cohort data in the form of arthroplasty registries strongly favors total knee arthroplasty over uni-compartmental knee arthroplasty. The Swedish Knee Arthroplasty Registry demonstrated higher revision rates with uni9s as compared with total knee replacements. In the Australian Joint Replacement Registry the cumulative 13 year percent revision rate for primary total knee replacements is 6.8% and for uni-compartmental knee replacements is 15.5%. Higher failure rates in uni-compartmental knee replacements seen in Australia has correlated to a significant decrease in the number of uni9s being performed, which peaked at 15.1% in 2003 and in 2014 has reduced to 4.7%. There is a direct correlation to age, with younger patients having a significantly higher percentage of revision following uni-compartmental knee replacements (25% failure rate at 11 years if less than 55 years old). There is also tremendous variability in the success rate of the uni in the Australian Registry depending on the implant design (5 year cumulative revision rate range 5.0% to 18.9%), which is simply not seen in the total knee replacement population (5 year cumulative revision rate range 1.6% to 7.7%). While one can perform the philosophical exercise of debating the merits of a total knee versus uni-compartmental knee, the evidence is overwhelming that in the hands of the masses a total knee replacement patient will have equal satisfaction to a uni-compartmental patient, and will enjoy a much lower probability of revision in the short term and in the long term.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".