Is There a Necessity for Individual Blood Water Corrections when Conductivity-Based Access Blood Flow Measurements Are Made?
Bibliographic record
Abstract
BACKGROUND/AIMS: Access blood water flow rate (Qaw) can be measured during hemodialysis using an online effective ionic dialysance (EID) methodology. Fresenius employ this methodology in their 2008K dialysis machine. The machine computer converts Qaw to an access blood flow rate (Fresenius Qa) using a generic blood water constant (BWC). We wished to validate this BWC. METHODS: 18 patients had Fresenius Qa measurements using the EID and these were compared with a 'gold standard' ultrasound dilution methodology (Transonic Qa). Qa values were also obtained by removing the BWC from Fresenius Qa values to obtain the Qaw and recorrecting it with individualized patient factors using hematocrit and total protein values (HctTp Qa). The measurements were repeated after 1 h. RESULTS: There were no significant differences between Fresenius and Transonic, nor between HctTp and Transonic Qa values (p > 0.17). There were strong correlations between both sets of values (r > 0.856; p < 0.001). There was a significant correlation between the pairs of Transonic Qa values (r = 0.823; p < 0.007), but not for Fresenius Qa pairs (r = 0.573; p > 0.07). It was surmised that the BWC was not valid post-dialysis. CONCLUSION: The generic BWC is comparable to individualized blood water correction factors when Qa measures are made early in dialysis and prior to ultrafiltration treatment.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".