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Record W2130983640 · doi:10.1016/j.jalz.2013.01.010

CSF biomarker variability in the Alzheimer's Association quality control program

2013· article· en· W2130983640 on OpenAlexaff
Niklas Mattsson, Ulf Andréasson, Staffan Persson, María C. Carrillo, Steven Collins, Sonia Chalbot, Neal E. Cutler, Diane Dufour‐Rainfray, Anne M. Fagan, Niels H. H. Heegaard, Ging‐Yuek Robin Hsiung, Bradley T. Hyman, Khalid Iqbal, D. Richard Lachno, Alberto Lleó, Piotr Lewczuk, José Luís Molinuevo, Piero Parchi, Axel Regeniter, Robert A. Rissman, Hanna Rosenmann, Giuseppe Sancesario, Johannes Schröder, Leslie M. Shaw, Charlotte E. Teunissen, John Q. Trojanowski, Hugo Vanderstichele, Manu Vandijck, Marcel M. Verbeek, Henrik Zetterberg, Kaj Blennow, Stephan A. Käser, Aladro José A. Rojo, Marilyn Albert, Daniel Alcolea, Anna Antonell, Hiroyuki Arai, Silvana Archetti, Eva Lagberg Arkblad, Inês Baldeiras, Aleš Bartoš, Dev Batish, Aurélie Bedel, Danièle Bentué‐Ferrer, Flora Berisha, Sergio Bernardini, Marinus A. Blankenstein, Olivier Bousiges, Michael C. Camuso, Maria Carrillo, Tiziana Casoli, Sebastiano Cavallaro, Odete Cruz e Silva, I. Cuvelier, Odile Delaroche, Roy B. Dyer, Sebastiaan Engelborghs, Anne Fogli, Orestes Vicente Forlenza, Nick C. Fox, Giovanni B. Frisoni, Daniela Galimberti, Elisabetta Galloni, Silvana Maria Gritti, Karen H. Gylys, Harald Hampel, Sabine Haustein, Theresa Heath, Michael T. Heneka, Sanna‐Kaisa Herukka, David M. Holtzman, Christian Humpel, Takeshi Iwatsubo, Khalid Iqbal, Claude Jardel, Mathias Jucker, Elisabeth Kapaki, Daniel Kidd, Péter Klivènyi, Ryozo Kuwano, Foudil Lamari, Jean Laplanche, Jordan Laser, Sylvian Lehmann, Qiao‐Xin Li, Walter Maetzler, Catherine Malaplate‐Armand, Ralph Martin, Robert Martone, Colin L. Masters, Marc Mercken, Tom Montine, William Nowatzke, Markus Otto, Xavier Parent, Lucilla Parnetti, Ronald C. Petersen, Koen Poesen, Isabelle Quadrio, Muriel Quillard, Zdeněk Rohan, Christin Sisowath, Anders Skinningsrud, Holly Soares, Hilkka Soininen, Knudsen Cindy Søndersø, Annette Spreer, Silvia Suardi, Robert M. Umek, Bianca Van Broeck, Rik Vandenberghe, László Vécsei, Igor Voštiar, Manfred Windisch

Bibliographic record

VenueAlzheimer s & Dementia · 2013
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of British Columbia
FundersNational Institute on AgingAlzheimer's Association
KeywordsBiomarkerImmunoassayNeurochemistryMedicineOncologyInternal medicineBiologyNeurologyImmunologyBiochemistryAntibodyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The cerebrospinal fluid (CSF) biomarkers amyloid beta 1-42, total tau, and phosphorylated tau are used increasingly for Alzheimer's disease (AD) research and patient management. However, there are large variations in biomarker measurements among and within laboratories. METHODS: Data from the first nine rounds of the Alzheimer's Association quality control program was used to define the extent and sources of analytical variability. In each round, three CSF samples prepared at the Clinical Neurochemistry Laboratory (Mölndal, Sweden) were analyzed by single-analyte enzyme-linked immunosorbent assay (ELISA), a multiplexing xMAP assay, or an immunoassay with electrochemoluminescence detection. RESULTS: A total of 84 laboratories participated. Coefficients of variation (CVs) between laboratories were around 20% to 30%; within-run CVs, less than 5% to 10%; and longitudinal within-laboratory CVs, 5% to 19%. Interestingly, longitudinal within-laboratory CV differed between biomarkers at individual laboratories, suggesting that a component of it was assay dependent. Variability between kit lots and between laboratories both had a major influence on amyloid beta 1-42 measurements, but for total tau and phosphorylated tau, between-kit lot effects were much less than between-laboratory effects. Despite the measurement variability, the between-laboratory consistency in classification of samples (using prehoc-derived cutoffs for AD) was high (>90% in 15 of 18 samples for ELISA and in 12 of 18 samples for xMAP). CONCLUSIONS: The overall variability remains too high to allow assignment of universal biomarker cutoff values for a specific intended use. Each laboratory must ensure longitudinal stability in its measurements and use internally qualified cutoff levels. Further standardization of laboratory procedures and improvement of kit performance will likely increase the usefulness of CSF AD biomarkers for researchers and clinicians.

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)0.185
metaresearch head score (Gemma)0.199
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1850.199
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0030.003
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 head0.041
GPT teacher head0.357
Teacher spread0.315 · 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.

Study designObservational
DomainEvaluation
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

Citations385
Published2013
Admission routes1
Has abstractyes

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