Simplified approaches for estimating vitamin A stores and β‐carotene bioconversion in humans (39.6)
Bibliographic record
Abstract
Better methods are needed to assess vitamin A (VA) status and the efficiency of bioconversion of β‐carotene (BC) to retinol (ROH). Data on plasma ROH kinetics from 2 h to 14 d after an oral tracer dose of [ 13 C 10 ]BC and [ 13 C 10 ]retinyl acetate (RAc) to 33 healthy young adults were analyzed using model‐based compartmental analysis (WinSAAM, the Windows version of the Simulation, Analysis and Modeling software). The 6‐compartment model that fit data for all subjects predicted 5.3 pools of plasma ROH were transferred into extravascular stores each day, with ~17%/d recycling back to plasma; 6.5%/day was irreversibly lost and total body VA stores (TBS) were 146 ± 89 μmol (mean ± SD). We derived a simplified isotope dilution (“Olson”) equation for TBS, eliminating several factors and assumptions: TBS = F * (1 / SA), where F (fraction of dose [FD] absorbed and retained) was estimated as 0.61 from the kinetic data and SA (specific activity) is FD 13 C 10 [ROH] in plasma 3 d after dosing / plasma ROH pool (μmol). TBS calculated using the equation was 149 ± 85 μmol, essentially the same as the value predicted by the model. BC bioconversion, calculated as FD 13 C 5 [ROH] (derived from BC) / 13 C 10 [ROH] (derived from RAc) at 2 d, averaged 23 ± 11% and was significantly correlated (R=0.942) with WinSAAM’s estimate based on areas under the curves (26 ± 12%). Our results indicate that both TBS and BC conversion to ROH can be estimated based on blood samples taken 3 and 2 d, respectively, after dosing. With further refinement, one sample at 3 d may suffice. Grant Funding Source : BBSRC, U.K. and DSM, Basel, Switzerland
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".