A Patient‐Focused Approach to Thrombolytic Use in the Management of Catheter Malfunction
Bibliographic record
Abstract
ABSTRACT Thrombus‐related catheter malfunction is a significant problem for catheter‐dependent dialysis patients. The primary medical intervention is the local luminal installation of thrombolytic agents (TLAs). There are three major TLA installation methodologies: locking, push, and infusion protocols. A systematic literature review of existing TLA protocols for treating dialysis catheter malfunction was performed using the PubMed and EMBASE (Drugs and Pharmacology) databases from the time of each database's inception to August 2005. Thrombolytic administration was then categorized according to the patient's clinical need: (1) an acute/immediate requirement, such as when malfunction prohibits dialysis initiation, and (2) rescue therapy, such as when the thrombus threatens to significantly impair current or subsequent dialysis clearance. Published TLA protocols are discussed in the context of their clinical requirement (acute or rescue therapy). A unifying clinically relevant management algorithm that considers the etiology of catheter malfunction as being thrombus related or not, and an approach to TLA use based on clinical presentation is described. This algorithm was developed after a systematic review of the literature. The application of this treatment algorithm requires prospective validation and study.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".