Validation of Tikhonov adaptively regularized gamma variate fitting with 24-h plasma clearance in cirrhotic patients with ascites
Bibliographic record
Abstract
PURPOSE: The aim was to compare late-time extrapolation of plasma clearance (CL) from Tikhonov adaptively regularized gamma variate fitting (Tk-GV) and from mono-exponential (E1) fitting. METHODS: Ten (51)Cr-ethylenediaminetetraacetic acid bolus IV studies in adults--8 with ascites--assessed for liver transplantation, with 12-16 plasma samples drawn from 5-min to 24-h, were fit with Tk-GV and E1 models and CL results were compared using Passing-Bablok fitting. RESULTS: The 24-h CL(Tk-GV) values ranged from 11.4 to 79.7 ml/min. Linear regression of 4- versus 24-h CL(Tk-GV) yielded no significant departure from a slope of 1, whereas the 4- versus 24-h CL(E1) slope, 1.56, was significantly increased. For CL(Tk-GV-24-h) versus CL(E1-24-h), there was a biased slope and intercept (0.85, 5.97 ml/min). Moreover, the quality of fitting of 24-h data was significantly better for Tk-GV than for E1, as follows. For 10 logarithm of concentration curves, higher r values were obtained for each Tk-GV fit (median 0.998) than for its corresponding E1 fit (median 0.965), with p < 0.0001 (paired t-test of z-statistics from Fisher r-z transformations). The E1 fit quality degraded with increasing V/W [volume of distribution (l) per kg body weight, p = 0.003]. However, Tk-GV fit quality versus V/W was uncorrelated (p = 0.8). CONCLUSION: CL(E1) values were dependent on sample time and the quality of fit was poor and degraded with increasing ascites, consistent with current opinion that CL(E1) is contraindicated in ascitic patients. CL(Tk-GV) was relatively more accurate and the good quality of fit was unaffected by ascites. CL(Tk-GV) was the preferred method for the accurate calculation of CL and was useful despite liver failure and ascites.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".