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Record W2014643838 · doi:10.1039/c1lc20524b

A digital microfluidic method for dried blood spot analysis

2011· article· en· W2014643838 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLab on a Chip · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern OntarioNewborn Screening OntarioUniversity of Toronto
FundersCanada Research Chairs
KeywordsDried blood spotDried bloodAnalyteMicrofluidicsChromatographyComputer scienceSpotsAutomationChemistryNanotechnologyMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Blood samples stored as dried blood spots (DBSs) are emerging as a useful sampling and storage vehicle for a wide range of applications. Unfortunately, the surging popularity of DBS samples has not yet been accompanied by an improvement in automated techniques for extraction and analysis. As a first step towards overcoming this challenge, we have developed a prototype microfluidic system for quantification of amino acids in dried blood spots, in which analytes are extracted, mixed with internal standards, derivatized, and reconstituted for analysis by (off-line and in-line) tandem mass spectrometry. The new method is fast, robust, precise, and most importantly, compatible with automation. We propose that the new method can potentially contribute to a new generation of analytical techniques for quantifying analytes in DBS samples for a wide range of applications.

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.

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 score0.363
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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