Increase in Outpatient Knee Arthroscopy in the United States: A Comparison of National Surveys of Ambulatory Surgery, 1996 and 2006
Bibliographic record
Abstract
BACKGROUND: This study was proposed to investigate the changes in the utilization of knee arthroscopy in an ambulatory setting over the past decade in the United States as well as its implications. METHODS: The National Survey of Ambulatory Surgery, last carried out in 1996, was conducted again in 2006 by the Centers for Disease Control and Prevention. We analyzed the cases with procedure coding indicative of knee arthroscopy or anterior cruciate ligament reconstruction. To produce estimates for all arthroscopic procedures on the knee in an ambulatory setting in the United States for each year, we performed a design-based statistical analysis. RESULTS: The number of arthroscopic procedures on the knee increased 49% between 1996 and 2006. While the number of arthroscopic procedures for knee injury had dramatically increased, arthroscopic procedures for knee osteoarthritis had decreased. In 1996, knee arthroscopies performed in freestanding ambulatory surgery centers comprised only 15% of all orthopaedic procedures, but the proportion increased to 51% in 2006. There was a large increase in knee arthroscopy among middle-aged patients regardless of sex. In 2006, >99% of arthroscopic procedures on the knee were in an outpatient setting. Approximately 984,607 arthroscopic procedures on the knee (95% confidence interval, 895,999 to 1,073,215) were performed in an outpatient setting in 2006. Among those, 127,446 procedures (95% confidence interval, 95,124 to 159,768) were for anterior cruciate ligament reconstruction. Nearly 500,000 arthroscopic procedures were performed for medial or lateral meniscal tears. CONCLUSIONS: This study revealed that the knee arthroscopy rate in the United States was more than twofold higher than in England or Ontario, Canada, in 2006. Our study found that nearly half of the knee arthroscopic procedures were performed for meniscal tears. Meniscal damage, detected by magnetic resonance imaging, is commonly assumed to be the source of pain and symptoms. Further study is imperative to better define the symptoms, physical findings, and radiographic findings that are predictive of successful arthroscopic treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".