Does the presence of an arteriovenous fistula alter changes in body water following hemodialysis as determined by multifrequency bioelectrical impedance assessment?
Bibliographic record
Abstract
Multifrequency bioelectrical impedance assessments (MFBIAs) aid clinical assessment of hydration status for hemodialysis (HD) patients. Many MFBIA devices are restricted to whole body measurements and as many patients dialyze using arteriovenous fistulas (AVFs), we wished to determine whether AVFs affected body water measurements. We reviewed pre- and post-HD segmental MFBIA measurements in 229 patients attending for midweek HD sessions. Up to 144 were dialyzed with a left arm AVF (L-AVF), 42 with a right arm AVF (R-AVF), and 43 by central venous access catheter (CVC). Water content and lean tissue were greater in the left compared to right arm in those patients with L-AVFs both pre and post dialysis (pre 2.1 ± 0.7 vs. 2.0 ± 0.7 L, and post 1.9 ± 0.6 vs. 1.8 ± 0.6 L and pre 2.65 ± 0.9 vs. 2.56 ± 0.8 kg, and post 2.34 ± 0.8 vs. 2.48 ± 0.8 vs. 2.34 ± 0.8 kg, respectively) and were also greater in the right compared to left arm for those patients dialyzing with R-AVFs (pre-HD 1.92 ± 0.5 vs. 1.86 ± 0.6 L and post-HD 1.79 ± 0.5 vs. 1.7 ± 0.5 L, and pre-HD 2.47 ± 0.6 vs. 2.38 ± 0.7 kg and post-HD 2.3 ± 0.74 vs. 1.28 ± 0.7 kg, respectively), all Ps < 0.05. There were no significant differences in arm volumes or composition pre or post dialysis in those dialyzing with CVCs. Segmental MFBIA detects differences in arm water and lean mass in patients with AVFs. The presence on an AVF increases the water content in the ipsilateral arm both pre and post HD. This increased water content of the fistula arm will not be detected by whole body bioimpedance devices.
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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 | 0.003 |
| 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".