Trends in knee arthroscopy utilization: a gap in knowledge translation
Bibliographic record
Abstract
PURPOSE: To evaluate the longitudinal trends in knee arthroscopy utilization in relation to published negative randomized controlled trials, focusing on annual rates, patient demographics and associated 30-day post-operative complications. METHODS: The American College of Surgeons National Surgical Quality Improvement Program database was queried using Current Procedural Terminology billing codes to identify arthroscopy cases between 2006 and 2016. 30-day post-operative complications were identified, and potential risk factors analysed using univariate and multivariate analyses. RESULTS: 68,346 patients underwent knee arthroscopy, of which 47,446 (69.5%) represented partial meniscectomies. The annual procedural rate, as a proportion of all reported cases, increased significantly from 2006 (0.3%) to 2016 (1.6%; p < 0.001), along with a significant increase in average patient age (44.3 ± 15.5 to 48.4 ± 14.5; p < 0.001). Specifically focusing on the meniscectomy cohort, average patient age significantly increased from 47.9 ± 15.1 to 50.7 ± 13.5 (p = 0.001). The overall incidence of complications was 2.0% (n = 1333), with major complications in 0.9% (n = 639) and minor complications in 1.0% (n = 701). Common complications included a return to the operating room (0.5%), deep vein thrombosis/thrombophlebitis (0.4%), and superficial infection (0.2%). Operating time > 90 min, diabetes, steroid use, ASA class 2+, and dialysis-dependency were the predictors of overall complication rates. CONCLUSION: Despite the publication of negative trials and new clinical practice guidelines, knee arthroscopy utilization and average patient age continue to increase. Given the high utilization, even low adverse event rates equate to substantial numbers of patients with minor and major complications. The NSQIP data show a gap in knowledge translation to clinical practice and highlight the need for improved clinical guidelines. LEVEL OF EVIDENCE: Cohort study; Level III.
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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 | 0.043 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".