Surgical approaches for treatment of osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is a major cause of disability in persons older than 50 years of age. The cost of the treatment of osteoarthritis and other related chronic joint disorders in the USA is estimated at over US$125 billion annually. Approximately 15% of Americans were affected by arthritis in 1995 and the prevalence is expected to increase to over 18% by 2020 [1]. The significant increase in the prevalence of this disease has increased the demand for surgical treatments. In 2006, over 500,000 total knee replacements and over 250,000 hip replacements were performed in the USA, and this number is increasing at the rate of 10% for knee replacements and 2.5% for hip replacements per year [2]. While nonoperative treatment is the initial treatment approach to OA, for patients who have significant pain and functional disability despite conservative treatment, surgery may provide substantial benefits. Long-term outcomes of total joint replacement have demonstrated predictable, consistent satisfactory reduction in pain and improved function. However, the beneficial results of total joint arthroplasty must be tempered by the possibility of failure secondary to mechanical and biological problems and unsatisfactory outcome. This chapter will highlight the surgical procedures that are presently available for the treatment of significant OA. An overview of the indications and expectations for the surgical procedures is presented. In addition, some of the controversies associated with each treatment modality will be provided.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".