Endoluminal dilatation for embedded hemodialysis catheters: A case-control study of factors associated with embedding and clinical outcomes
Bibliographic record
Abstract
BACKGROUND: With the increasing frequency of tunneled hemodialysis catheter use there is a parallel increase in the need for removal and/or exchange. A small but significant minority of catheters become embedded or 'stuck' and cannot be removed by traditional means. Management of embedded catheters involves cutting the catheter, burying the retained fragment with a subsequent increased risk of infections and thrombosis. Endoluminal dilatation may provide a potential safe and effective technique for removing embedded catheters, however, to date, there is a paucity of data. OBJECTIVES: 1) To determine factors associated with catheters becoming embedded and 2) to determine outcomes associated with endoluminal dilatation. METHODS: All patients with endoluminal dilatation for embedded catheters at our institution since Jan. 2010 were included. Patients who had an embedded catheter were matched 1:3 with patients with uncomplicated catheter removal. Baseline patient and catheter characteristics were compared. Outcomes included procedural success and procedure-related infection. Logistic regression models were used to determine factors associated with embedded catheters. RESULTS: We matched 15 cases of embedded tunneled catheters with 45 controls. Among patients with embedded catheters, there were no complications with endoluminal dilatation. Factors independently associated with embedded catheters included catheter dwell time (> 2 years) and history of central venous stenosis. CONCLUSION: Embedded catheters can be successfully managed by endoluminal dilatation with minimal complications and factors associated with embedding include dwell times > 2 years and/or with a history of central venous stenosis.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".