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Record W2971435724 · doi:10.1016/j.clinms.2019.09.002

Tandem mass spectrometry in the clinical laboratory: A tutorial overview

2019· article· en· W2971435724 on OpenAlex
J. Grace van der Gugten

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

VenueClinical mass spectrometry · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsProvincial Health Services Authority
Fundersnot available
KeywordsTandem mass spectrometryTandemMass spectrometryChromatographyTriple quadrupole mass spectrometerLiquid chromatography–mass spectrometryChemistryImmunoassayComputer scienceSelected reaction monitoringMedicineMaterials science

Abstract

fetched live from OpenAlex

Tandem mass spectrometry (tandem MS) is a powerful technique that directly measures compounds based on their molecular weight. Liquid chromatography triple quadrupole mass spectrometry (LC-MS/MS), a type of tandem MS, has been increasingly adopted by clinical laboratories in recent years. While it is complicated and challenging, it has selectivity and accuracy advantages over more traditional laboratory techniques such as immunoassay. Able to measure a wide range of compounds, when correctly utilized and implemented, LC-MS/MS can benefit the clinical laboratory and improve patient care. This mini review is an introduction to clinical LC-MS/MS for the novice user.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.356
Teacher spread0.318 · 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