Temporal trends in peripheral arterial interventions: Observations from the blue cross blue shield of Michigan cardiovascular consortium (BMC2 PVI)
Bibliographic record
Abstract
OBJECTIVES: The aim is to examine trends in procedural indication, arterial beds treated, and device usage in peripheral arterial interventions (PVIs). BACKGROUND: There is little data on indication, vascular beds treated and devices utilized for peripheral arterial interventions. METHODS: We used data from 43 hospitals participating in the BMC2 VIC registry. PVIs were separated by year and divided by arterial segment. Lower extremity PVIs were subclassified as having been performed for claudication or critical limb ischemia (CLI). Yearly device usage was also included. A repeated measure ANOVA was used to determine trends. RESULTS: 44,650 PVIs were performed from 2006 to 2013. Renal interventions decreased from 18% of interventions in 2006 to 5.6% in 2013 (P < 0.001) and femoral-popliteal increased from 54.9% in 2006 to 64.5% in 2013 (P < 0.001). No significant trend was seen for aorta-iliac or below-the-knee interventions. 58.6% of PVIs were performed for claudication in 2006 and this decreased to 44.6% in 2013 (P = 0.025). Indications for CLI were 24.1% in 2006 and 47.5% in 2013 (P < 0.001). There were significant increases in the use of balloon angioplasty (P = 0.029) and cutting/scoring balloons (P < 0.001) while cryoballoon usage decreased (P < 0.001). No significant changes were found with stenting, atherectomy, and laser. CONCLUSIONS: There is a significant increase in patients presenting with CLI. Renal artery intervention rates are decreasing while femoral-popliteal interventions are increasing. Additionally, balloon angioplasty and cutting/scoring balloon usage is increasing. © 2017 Wiley Periodicals, Inc.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".