Bibliographic record
Abstract
The influence of dialysis prescription on outcome is well established, and currently the amount of dialysis prescribed is based on small molecular weight toxin removal as represented by the clearance of urea. The "normalized dose of dialysis" (Kt/V(urea)) concept is well established. Most techniques for dialysis quantification require that blood samples be taken at the beginning and after the completion of dialysis. The postdialysis sample, however, gives cause for concern because of the "rebound phenomenon" due to nonuniform distribution of urea among body compartments. Blood samples give "indirect" measures of dialysis quantification. Thus direct urea concentration measurements in dialysate may be superior in urea kinetic modeling and these may be made "real time" during dialysis. It is with real-time monitoring that future advances in dialysis quantification will take place. These will be of two types. The first will analyze blood water or dialysate samples for urea content multiple times throughout the treatment; the second will assess the on-line clearance of urea using surrogate molecules such as sodium chloride, the clearance being determined by conductivity measurements. On-line urea monitoring is based on the action of urease on urea in a water solution and measurement of the resultant ammonium ions, which are measured directly by a specific electrode or indirectly by conductivity changes. Differences in blood-side versus dialysate-side urea monitors exist which reflect the parameters they can provide, but with both, the standard urea kinetic measurements of Kt/V and nPCR (nPNA) are easily obtainable. A range of additional parameters can be derived from dialysate-side monitoring such as "whole-body Kt/V," "pretreatment urea mass" and "whole-body urea clearance," which are worthy of future studies to determine their roles in adequacy assessment. Conductivity clearance measurements are made by examining the conductivity differences between dialysate inlet and outlet measured at two different dialysate inlet concentrations. This allows for the calculation of the electrolyte (ionic) dialysance, which is equal to the "effective" urea clearance, that is, the clearance that takes into account recirculation effects that reduce hemodialysis efficiency. The continuous reading of effective ionic clearance will allow an average value for K to be obtained for that dialysis, and hence the parameter K x t as an indication of dialysis dose is easily and accurately obtained for every treatment. The conductivity technology is cheap and rugged, and thus expanded use can be expected. Urea monitors have an inherent cost and require maintenance, and perhaps will remain researchers' tools for the present. The methodologies can complement each other; the addition of an accurate and independent value for K to dialysate based urea monitoring is like having simultaneous blood- and dialysate-side monitoring, and allows further increase in measurable parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".