MétaCan
Menu
Back to cohort
Record W4390910595 · doi:10.4155/bio-2023-0180

Validation of Mitra <sup>®</sup> VAMS <sup>®</sup> as a Blood Collection Technique for Trace Elements Analysis Using ICP-MS/MS

2024· article· en· W4390910595 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.

Bibliographic record

VenueBioanalysis · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsUniversité du Québec à Trois-RivièresInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsVenipunctureInductively coupled plasma mass spectrometryBlood samplingChromatographySampling (signal processing)TRACE (psycholinguistics)ChemistryMass spectrometryComputer scienceMedical physicsMedicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Background: Clinical dosage of toxic and essential elements in blood is well established and the collection method is still by venipuncture. This method has drawbacks and is not suited for everyone. Volumetric absorptive microsampling (VAMS) has been shown to have advantages over venipuncture. Materials & methods: Using inductively coupled plasma tandem mass spectrometry, a method for quantifying elements in whole blood sampled on VAMS was developed/validated. Method's performance was assessed by comparison with whole blood results. Results: Validation and performance assessment tests tend to show that most of the targeted elements provides accurate and reproducible results comparing to a method of reference. Conclusion: Overall, VAMS presents good preliminary results to eventually become an alternative to venipuncture for blood sampling for some trace elements analysis purposes.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.316
Teacher spread0.289 · 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