Objective Performance Goals Based on a Systematic Review and Meta-Analysis of Clinical Outcomes for Bare-Metal Stents and Percutaneous Transluminal Angioplasty for Hemodialysis-Related Central Venous Obstruction
Bibliographic record
Abstract
PURPOSE: To use safety and efficacy outcomes following treatment with percutaneous transluminal angioplasty (PTA) and/or stent placement for thoracic central venous obstruction in hemodialysis-dependent patients to establish objective performance goals (OPGs). METHODS: A systematic literature review and meta-analysis were conducted for articles published between January 1, 2000, and August 31, 2021. Efficacy outcomes included primary patency rates at 6 and 12 months, and safety outcomes included adverse events (AEs) categorized as access loss, procedure-related AEs, and serious AEs (SAEs). OPGs were derived from the upper and lower bounds of the 95% confidence intervals for primary patency and SAE rates. RESULTS: Of 66 articles reviewed, 17 met the inclusion criteria (PTA, n = 4; stent placement, n = 5; PTA/stent, n = 8). The 6- and 12-month primary patency rates for PTA were 50.9% and 36.7%, respectively. Based on these findings, the proposed 6- and 12-month primary patency OPGs identifying superiority against PTA were 66.5% and 52.6%, respectively, and those for noninferiority were 39.0% and 25.7%, respectively. For stent placement, the 6- and 12-month primary patency rates were 69.7% and 47.9%, respectively. The proposed 6- and 12-month primary patency OPGs identifying superiority were 82.1% and 64.1%, respectively, and those for noninferiority were 59.3% and 35.8%, respectively. SAE rates for PTA and stent placement were 3.8% and 8.1%, respectively. Proposed safety OPGs for noninferiority versus superiority for PTA and stent placement were 10.1% versus 1.4% and 13.6% versus 4.8%, respectively. CONCLUSION: The OPGs derived from real-world studies of PTA and stent placement may serve as a benchmark for future interventions indicated for this patient population.
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 | 0.087 | 0.126 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.051 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".