Performance properties of filter-paper used in blood spot collection devices for quantitation of phenylalanine
Bibliographic record
Abstract
AIMS: Accurate and precise measurement of dried blood spot (DBS) phenylalanine (Phe) is vital for managing phenylketonuria (PKU). Standard DBS collection devices use grade-226 filter-paper, while the CapitainerB quantitative device utilizes grade-222 filter-paper. Although grade-226 filter-paper performance is well characterized, data on grade-222 filter-paper are sparse. This study aimed to investigate the analytical properties of grade-222 and grade-226 filter-papers. MATERIALS AND METHODS: We compared grade-222 and grade-226 filter-papers for Phe measurement accuracy and imprecision in DBS generated using both filter-papers. Scanning electron microscopy (SEM) and slit lamp imaging were used to assess the physical properties of the filter-papers. RESULTS: Using an aqueous calibrator as reference, grade-222 exhibited a mean bias of -1.1%, the mean bias for grade-226 was -7.3%. Intra-assay imprecision was 2.3% for grade-222, versus 4.2% for grade-226. SEM revealed that fibers in grade-226 filter-paper are bonded by an amorphous material, which is absent in grade-222 filter-paper. Total error analysis indicated grade-222 filter-paper reduced uncertainty of Phe measurement compared to grade-226 filter-paper. CONCLUSIONS: Grade-222 filter-paper was proven to have superior analytical performance for Phe quantification, providing improved differentiation between safe and harmful Phe concentrations and offering more reliable PKU monitoring compared to traditional grade-226 filter-paper.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| 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".