Transonic, thermodilution, or ionic dialysance to manage vascular access: Which method is best?
Bibliographic record
Abstract
Regularly monitoring blood flow through a vascular access (Qa) can predict a dysfunction and dramatically reduce the number of thromboses. The aim of our study was to compare two integrated access flow devices, thermodilution (Qa-BTM: BTM(®), Fresenius Medical Care, Bad Homburg, Germany) and ionic dialysance (Qa-ID: OCM(®), Fresenius Medical Care, Bad Homburg, Germany), with the "gold standard" saline dilution (Qa-T: Transonic(®), Systems Inc., Ithaca, NY, USA). Measurements were performed sequentially and were repeated in the first 90 minutes of a single dialysis session in 24 long-term hemodialysis patients with a vascular access. Bland-Altman, linear regression (r(2)), and intraclass correlation coefficients (ICC) assessed reproducibility, correlations, and concordance between the techniques. Average access flow for Qa-T was 1549 (± 844) mL/minute, Qa-BTM was 1530 (± 856) mL/minute (P = NS), and Qa-ID was 1619 (± 1085) mL/minute (P = NS). Respectively, ICC, (r(2)), and bias were 0.99, (0.98), and -19 mL/minute for Qa-BTM, and 0.75, (0.65), and +69 mL/minute for Qa-ID. The limits of agreement were -287 to +250 mL/minute for Qa-BTM and -1647 to +1785 mL/minute for Qa-ID. Reproducibility of thermodilution and ionic dialysance, expressed as relative differences, was not significantly different from saline dilution. Recirculation, measured by saline dilution, was 0% (0-4%), the same as the 0% measured by thermodilution, with correct placement of bloodlines and corrected for cardiopulmonary recirculation. The integrated access flow measurement devices, thermodilution and ionic dialysance, are reasonable alternatives to using saline dilution to measure Qa: Thermodilution showed better precision and correlation. They are reliable, make monitoring of vascular access easier, incur no extra costs, and use no additional consumables.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".