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Record W4406210954 · doi:10.1002/alz.090643

International Initiative for Harmonization of Plasma Neurofilament light chain NfL clinical reporting in neurodegenerative diseases

2024· article· en· W4406210954 on OpenAlexaff
Constance Delaby, Charlotte E. Teunissen, Dorte Aalund Olsen, Daniel Alcolea, Alberto Lleó, Alicia Algeciras‐Schimnich, Gustavo Alves Andrade dos Santos, Élodie Bouaziz-Amar, Xavier Ayrignac, Inês Baldeiras, Edith Bigot‐Corbel, Maria Bjerke, Tiziana Casoli, Tinatin Chabrashvili, Miles Chapman, Jessica Cusato, Erdinç Dursun, Anthony Fourier, Daniela Galimberti, Duygu Gezen‐Ak, Brian A. Gordon, Melanie Hart, Marina Herwerth, Flora Kaczorowski, Kensaku Kasuga, Ashvini Keshavan, Michael Khalil, Pèter Köertvelyessy, Jens Kühle, Aurélie Ladang, Piotr Lewczuk, Giancarlo Logroscino, Magda Tsolaki, Guido Maria Giuffrè, M Blanc, Barbara Mroszko, Giulia Musso, Agnieszka Kulczyńska‐Przybik, Léonor Nogueira, Claire Paquet, Piero Parchi, Lucilla Parnetti, Raquel Perez Garay, Koen Poesen, Isabelle Quadrio, Muriel Quillard‐Muraine, Enrique Rodriguez Borja, Susanna Schraen, Daniela Terracciano, Hayrettin Tumani, Socrates J. Tzartos, Nadine Unterwalder, Lisa Vermunt, Cheryl L. Wellington, Jose Yriarte, Kaj Blennow, Henrik Zetterberg, Sylvain Lehmann

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarmonizationBusinessMedicine

Abstract

fetched live from OpenAlex

Abstract Background The quantification of neurofilament light chain (NfL) in blood and cerebrospinal fluid (CSF) has proved useful in many contexts, for the diagnosis and prognosis of various neurological disorders. There is, however, a diversity of practices between centers, essentially linked to the context of use (COU), analytical methods, consideration of comorbidities, determination of cut‐points or use of interpretation scales. Finally, for the same biochemical profile, the interpretation and reporting of results may differ from one center to another, raising the question of test commutability. To date, no consensus has been reached between the different laboratories involved to define the most appropriate conclusions/comments based on COU and cut‐points. This work is an essential step towards consensual harmonization of the clinical use of NfL after CSF and/or blood analysis in various neurological contexts, as advocated by the Alzheimer's Association "Biofluid Based Biomarkers PIA" working group. Method This international project involves 58 clinical laboratories in 16 countries, specializing in the biochemical diagnosis of neurological disorders. By means of a questionnaire, we obtained a description of the COU, pre‐analytical and analytical (biological fluid and method used to quantify NfL) protocols of all the centers involved. Results Of the centers, 42% quantified NfL in CSF, 29% in serum and 28% in plasma, and 1% in dried blood spot. The COUs were as follows: Frontotemporal dementia (FTD, 17%), Alzheimer's disease (AD, 16%), multiple sclerosis (MS, 16%), amyotrophic lateral sclerosis (ALS, 11%), psychiatric syndrome (PS, 10%), Creutzfeldt‐Jakob disease (CJD, 8%), Parkinson's disease (PD, 8%), peripheral neuropathy (PN, 7%) and traumatic brain injury (TBI, 7%). Most centers define pathological cut‐points based on published literature and take age into account (50%). Conclusion Our initial results highlight the state of the art in terms of the clinical use of NfL analysis in CSF and blood in the context of different neurological diseases. We have now defined a coordinator for each COU subgroup and are organizing consensus meetings to harmonize the use and reporting of NfL measurements for the identified clinical applications. The results of these next steps will be presented.

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)0.258
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.742
Threshold uncertainty score0.915

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2580.114
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0120.012
Science and technology studies0.0020.003
Scholarly communication0.0050.003
Open science0.0090.012
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.126
GPT teacher head0.399
Teacher spread0.273 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations0
Published2024
Admission routes1
Has abstractyes

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