Arthroscopic Débridement of the Knee for Osteoarthritis in Patients Fifty Years of Age or Older
Bibliographic record
Abstract
BACKGROUND: There is little available information regarding the effectiveness of arthroscopic knee débridement for the treatment of arthritis. The purpose of this study was to evaluate patterns of utilization of arthroscopic knee débridement and outcomes following that procedure for the treatment of degenerative arthritis in persons fifty years of age or older in the Province of Ontario. METHODS: All patients fifty years of age or older who underwent elective arthroscopic knee débridement for the treatment of degenerative arthritis between 1992 and 1996 were identified from administrative data sets. Surgical complications and subsequent knee replacements were noted. Population rates were compared across the sixteen District Health Council regions within Ontario. Outcomes were modeled as a function of patient age, gender, and comorbidity with use of multivariate regression analysis. RESULTS: We identified 14,391 eligible unilateral arthroscopic knee débridement procedures. There was a threefold difference in the population rate of arthroscopic débridement across geographic regions. Overall, 1330 (9.2%) of all patients required total knee arthroplasty within one year after the débridement. Of the 6212 patients with a minimum three-year follow-up, 1146 (18.4%) had undergone total knee replacement within three years following the débridement. Patients who were at least seventy years of age were 4.7 times more likely to have total knee arthroplasty within one year after the débridement than were those less than sixty years of age (19.0% compared with 4.0%; p < 0.05). Patients sixty years of age or older were more likely to have an early total knee replacement (within one year after the débridement) in District Health Council regions where the population rates of arthroscopic knee débridement were higher (p = 0.04). CONCLUSIONS: The higher rates of early total knee arthroplasty and the significant relationship between rates of early total knee arthroplasty and rates of utilization suggest that arthroscopic débridement for the treatment of osteoarthritis of the knee may be overutilized in elderly patients. Important clinical issues such as patient preference, risk perception, and functional outcome cannot be addressed just with the administrative data used for this study.
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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) | 0.000 | 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".