Low-Intensity Adjusted-Dose Warfarin for the Prevention of Hemodialysis Catheter Failure
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To determine whether warfarin prolongs the time to first mechanical-catheter failure. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This was a multicenter parallel-group randomized controlled trial with blinding of participants, trial staff, clinical staff, outcome assessors, and data analysts. Randomization was in a 1:1 ratio in blocks of four and was concealed by use of fax to a central pharmacy. Hemodialysis patients with newly-placed catheters received low-intensity monitored-dose warfarin, target international normalized ratio (INR) 1.5 to 1.9, or placebo, adjusted according to schedule of sham INR results. The primary outcome was time to first mechanical-catheter failure (inability to establish a circuit or blood flow less than 200 ml/min). RESULTS: We randomized 174 patients: 87 to warfarin and 87 to placebo. Warfarin was associated with a hazard ratio (HR) of 0.90 (P=0.60; 95% confidence interval [CI], 0.57, 1.38) for time to first mechanical-catheter failure. Secondary analyses were: time to first guidewire exchange or catheter removal for mechanical failure (HR 0.78; 95% CI, 0.37, 1.6); time to catheter removal for mechanical failure (HR 0.67; 95% CI, 0.19, 2.37); and time to catheter removal for any cause (HR 0.89; 95% CI, 0.42, 1.81). Major bleeding occurred in 10 participants assigned to warfarin and seven on placebo (relative risk, 1.43; 95% CI, 0.57, 3.58; P=0.61). CONCLUSIONS: We found no evidence for efficacy of low-intensity, monitored-dose warfarin in preventing mechanical-catheter failure.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".