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Record W4280593249 · doi:10.4155/bio-2022-0007

Methylmalonic Acid Analysis using Urine Filter Paper Samples to Screen for Metabolic Vitamin B <sub>12</sub> Deficiency in Older Adults

2022· article· en· W4280593249 on OpenAlexafffund
Michel Boutin, Nancy Presse, David Allard, Tristan Martineau, Pierrette Gaudreau, Christiane Auray‐Blais

Bibliographic record

VenueBioanalysis · 2022
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsCentre Hospitalier de l’Université de MontréalInstitut Universitaire de Gériatrie de MontréalUniversité de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersCanadian Institutes of Health ResearchRéseau québécois de recherche sur le vieillissement
KeywordsMethylmalonic acidUrineChromatographyCreatinineVitamin B12ChemistryFilter paperElutionVitamin bVitaminBiochemistry

Abstract

fetched live from OpenAlex

Aim: Methylmalonic acid (MMA) analysis in urine represents a noninvasive approach to screening for vitamin B12 deficiency in older adults. A method allowing the analysis of MMA/creatinine in fasting urine collected on filter paper was developed/validated. Method: Dry urine specimens were eluted using a solution containing internal standards, filtrated and analyzed by ultra-performance LC-MS/MS. Results: The method allowed the chromatographic separation of MMA from succinic acid. Dried urine samples were stable for 86 days at room temperature. The MMA/creatinine ratios measured in urine collected on filter paper were highly correlated with values derived from the corresponding liquid specimens. Conclusion: This robust filter paper method might greatly improve the accessibility and cost–effectiveness of vitamin B12 deficiency screening in older adults.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.032
GPT teacher head0.307
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2022
Admission routes2
Has abstractyes

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