Evaluation of partial pressure <scp>CO<sub>2</sub></scp> change in the dialyzer blood inlet during hemodialysis as a measure of vascular access recirculation
Bibliographic record
Abstract
Abstract Introduction Vascular access recirculation during hemodialysis is associated with reduced effectiveness and worse survival outcomes. To evaluate recirculation, an increase in pCO2 in the blood of the arterial line during hemodialysis (threshold of 4.5 mmHg) was proposed. The blood returning from the dialyzer in the venous line has significantly higher pCO2, so in the presence of recirculation, pCO2 in the arterial blood line may increase (ΔpCO2) during hemodialysis sessions. The aim of our study was to evaluate ΔpCO2 as a diagnostic tool for vascular access recirculation in chronic hemodialysis patients. Methods We evaluated vascular access recirculation with ΔpCO2 and compared it with the results of a urea recirculation test, which is the gold standard. ΔpCO2 was obtained from the difference in pCO2 in the arterial line at baseline (pCO2T1) and after 5 min of hemodialysis (pCO2T2). ∆pCO2 = pCO2T2–pCO2T1. Findings In 70 hemodialysis patients (mean age: 70.52 ± 13.97 years; hemodialysis vintage of 41.36 ± 34.54, KT/V 1.4 ± 0.3), ∆pCO2 was 4 ± 4 mmHg, and urea recirculation was 7% ± 9%. Vascular access recirculation was identified using both methods in 17 of 70 patients, who showed a ∆pCO2 of 10 ± 5 mmHg and urea recirculation of 20% ± 9%; time in months of hemodialysis was the only difference between vascular access recirculation and non‐vascular access recirculation patients (22 ± 19 vs. 46 ± 36, p: 0.05). In the non‐vascular access recirculation group, the average ΔpCO2 was 1.9 ± 2 (p: 0.001), and the urea recirculation % was 2.8 ± 3 (p: 0.001). The ΔpCO2 correlated with the urea recirculation % (R: 0.728; p < 0.001). Discussion ΔpCO2 in the arterial blood line during hemodialysis is an effective and reliable diagnostic tool for identifying recirculation of the vascular access but not its magnitude. The ΔpCO2 test application is simple and economical and does not require special equipment.
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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 | 0.001 | 0.001 |
| 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".