The effect of short‐term frozen storage of human milk on the validity of Vitamin A assays using the iCHECKTM rapid analyzer
Bibliographic record
Abstract
Background Breast milk retinol concentration is an indicator of both infant nutrient intake and population vitamin A status. Milk is easily and non‐invasively collected and rapid field methods for vitamin A analysis now exist. Objective To confirm or refute the assertion of restricting fluorescent assaying of milk vitamin A to “unfrozen” samples (Schweigert et al, Sight & Life Magazine 2011 (2) 18–22). Methods Full‐breast expression milk samples were collected in 21 women from Mam ‐Mayan rural communities in the Western Highlands of Guatemala. A milk‐fat packing volume (creamatocrit), expressed in volume percent (vol%), was measured on a hematocrit microcentrifuge. Retinol concentration was assayed, both fresh and after 20–26 d at −20°C, on an iCHECK TM rapid field analyzer (Bioanalyt, Telbow, Germany). Results Respective retinol concentrations were 539±221 μg/L (fresh) and 528±205 μg/L (post‐freezing) (p=0.45), with a Pearson correlation coefficient (P) and Spearman coefficient (S) both of r=0.959 (p=0.001). The corresponding values for creamatocrit were 8.1±3.1 vol% vs 9.0±4.0 vol% (p=0.17), with r=0.774 (p=0.001)(P) and r=0.844 (p=0.001)(S). Conclusions Milk samples can be stored for at least 3 wks without affecting the validity of iCHECK TM assays. Funded by Sight & Life of Basel and Tufts University School of Medicine
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".