Clinical Determination of Dry Body Weight
Bibliographic record
Abstract
While nephrologists wait for the ideal, non invasive, inexpensive, precise, and reproducible tool to evaluate extracellular volume (ECV), they need to exert their clinical acumen in the quest of that holy grail, dry weight (DW). Estimation of DW using a clinical approach based on blood pressure (BP) and ECV is feasible and reliable as shown by successful experiences in various dialysis modes over more than three decades. But a need still exists to resolve difficulties associated with accurate assessment of BP (methods and circumstances of measurement, and the confounding effects of antihypertensive drugs) and ECV (evaluation of weight changes unrelated to ECV, lack of specificity and sensitivity of clinical symptoms, lag time, confusion in terminology). An essential point in clinical assessment of DW is that a normal BP is at the same time the target and the crucial index of DW achievement. For this reason, a trialand-error "probe" process has to be used at intervals to make sure that the dry weight target point is correctly estimated. The various "non clinical" methods proposed for dry weight assessment increase the complexity and the cost of hemodialysis. They are, in the present state of things, more clinical research than practice tools. They do not replace clinical judgment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".