Predicting tissue plasminogen activator use and success in in-center hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: Hemodialysis catheter malfunction is problematic. We aimed to determine clinical, catheter, and hemodialysis variables that predict: (i) catheter malfunction requiring recombinant tissue-plasminogen activator (TPA) treatment and (ii) the success of TPA administration. METHODS: Clinical, catheter, and hemodialysis variables were collected from a cohort of 559 catheters (141,526 catheter days) in 175 hemodialysis patients (2008-2011). Time to, and predictors of, catheter malfunction were determined for all catheters and primary catheters only, analyzed by Cox-proportional hazard model, multi-variable logistic regression model, and mixed-effect logistic regression models. Success of TPA use was determined immediately, next day, and two weeks after use. RESULTS: Shorter time to first TPA administration in all patient's catheters was associated with increased body mass index (hazard ratio [HR] = 1.06, 1.01-1.11, p = 0.03) and being of black race (HR = 3.05, 1.65-5.67, p<0.01). Primary TPA administration success at two weeks among primary catheters was associated with increased mean peak venous pressure before TPA administration (odds ratio [OR] = 1.03, 1.01-1.06, p<0.01) and decreased mean of the lowest systolic blood pressure measure for the three dialysis sessions prior to TPA administration (OR = 0.95, 0.91-0.99, p = 0.02). TPA administration success at two weeks among all catheters was associated with decreasing body mass index (OR = 0.84, CI 0.73-0.96, p = 0.01) and having diabetes (OR = 7.19, 1.40-36.81, p = 0.02). CONCLUSIONS: Both patient and dialysis predictors of TPA use and success were identified in this study, which may be useful for fine-tuning catheter management protocols to target hemodialysis patients at high risk of catheter malfunction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, 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".