Regional anesthesia with epinephrine‐containing lidocaine reduces pericatheter bleeding after tunneled hemodialysis catheter placement
Bibliographic record
Abstract
INTRODUCTION: Pericatheter bleeding (PB) following tunneled hemodialysis catheter (THC) placement is a common phenomenon. In addition to complicating securement of the THC, the PB may loosen the adhesive catheter dressing and delay wound healing. The primary aim of this study was to determine whether epinephrine-containing local anesthetics rather than plain ones reduce superficial PB after THC placement. METHODS: The study was based on the retrospective analysis of the prospectively gathered data. Forty-six patients receiving local analgesia during THC placement were randomly assigned in a double-blind manner to two groups according to local anesthetic mixtures used (n =22 to prilocaine group [group 1]; n =24 to epinephrine-containing lidocaine group [group 2]). Presence or absence of PB after the THC placement was evaluated. Differences between groups with and without controlling other variables were statistically analyzed. FINDINGS: Epinephrine-containing lidocaine (group 2) significantly reduced PB in comparison with prilocaine, P = 0.003. Use of epinephrine-containing lidocaine (group 2) was associated with a reduction in the likelihood of PB (Odds ratio = 0.017). Meanwhile, use of prilocaine (group 1) had 59.7 times higher odds in the likelihood of PB after THC placement. Lower rate of systolic blood pressure (SBP) in group 2 patients after 5 minutes of injections was also noted, P = 0.008. Epinephrine-containing lidocaine was well tolerated and caused no significant cardiovascular disturbance. DISCUSSION: Local infiltration of epinephrine-containing lidocaine instead of plain local anesthetics during THC insertion may reduce superficial PB and improve patient comfort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".