Central Vein Stenosis: A Common Problem in Patients on Hemodialysis
Bibliographic record
Abstract
Central vein stenosis (CVS) has been associated with subclavian (SCV) catheter insertions. The prevalence of CVS in the current era with minimal use of SCV catheters is unknown. Furthermore, the prevalence of CVS in patients with access problems has not been previously described to our knowledge. We evaluated 235 prevalent patients on hemodialysis (HD), and, of these, 133 underwent venography for access related concerns over a 14 month period. Of these 133 patients, 55 (41%) had evidence of significant CVS on venogram. Patients with CVS had a longer duration on HD (43 +/- 12 months vs. 34 +/- 15 months, p = 0.018) and a history of a previous HD catheter insertion (52/55 patients vs. 59/78 patients, p = 0.0039). There were only 18 patients with a subclavian catheter insertion. In those with any history of previous HD catheter insertion, multivariate analysis demonstrated that number of catheters remains a significant factor (OR 2.69, p = 0.0004) even after excluding those subclavian insertions. This study demonstrates that CVS occurs in almost half of the patients with access problems undergoing venography. We confirm the important contribution of central vein cannulation to CVS and show that, despite minimizing subclavian catheter insertion, CVS remains a relatively common occurrence. Thus further studies should attempt to determine the true incidence of this problem and ultimately address the optimal treatment strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".