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Record W2990040262 · doi:10.1002/lipd.12203

Interlaboratory Assessment of Dried Blood Spot Fatty Acid Compositions

2019· article· en· W2990040262 on OpenAlexaff
Adam H. Metherel, William S. Harris, Ge Liu, Robert A. Gibson, Raphaël Chouinard‐Watkins, Richard P. Bazinet, Lei Liu, J. Thomas Brenna, Juan J. Aristizabal‐Henao, Ken D. Stark, Robert Block

Bibliographic record

VenueLipids · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsCanada Research ChairsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsPolyunsaturated fatty acidEicosapentaenoic acidDried blood spotFatty acidDocosahexaenoic acidArachidonic acidChemistryLipidologyFood scienceChromatographyBiochemistry

Abstract

fetched live from OpenAlex

Dried blood spots for fatty acid profiling are increasing in popularity; however, variability in results between laboratories has not been characterized. Whole blood from two subjects (low and high n-3 polyunsaturated fatty acid [PUFA] status) was collected, 25 μL applied to butylated hydroxytoluene (BHT)-treated chromatography strips, dried in air, and shipped to five laboratories. Results were reported as "routine" (typical fatty acids for each laboratory) or "standardized" (a set of 19 fatty acids), and outliers and variability (%CV) were determined. Five and eight outliers of a possible 91 measures each were identified by routine and standardized reporting, respectively, including eicosapentaenoic acid (EPA, 20:5n-3) in the low n-3 PUFA sample and arachidonic acid in the high n-3 PUFA sample. By standardized reporting, no outliers were identified for EPA or docosahexaenoic acid (DHA, 22:6n-3), and %CV decreased from 8.6% to 6.0% and 9.1% to 6.6% for EPA and 10.5% to 7.2% and 10.5% to 6.6% for DHA in the low and high n-3 PUFA sample, respectively. In conclusion, fatty acid profiles yielded few outliers, and standardization of reporting reduced the variability between laboratories.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.256
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

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