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Record W3113986157 · doi:10.1016/j.neulet.2020.135593

Neurofilament light plasma concentration positively associates with age and negatively associates with weight and height in the dog

2020· article· en· W3113986157 on OpenAlexaboutno aff
Jackie Perino, Margaret Patterson, Mehdi Momen, Mina Borisova, Amanda Heslegrave, Henrik Zetterberg, Jordan Gruel, Emily E. Binversie, Lauren Baker, John Svaren, Susannah J. Sample

Bibliographic record

VenueNeuroscience Letters · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsnot available
FundersU.S. National Library of MedicineNational Institutes of HealthVetenskapsrådetEuropean Research CouncilUK Dementia Research InstituteAlzheimer's Drug Discovery Foundation
KeywordsLabrador RetrieverCoatBiomarkerBreedPopulationBody weightNeurofilamentPhysiologyMedicinePlasma concentrationBiologyEndocrinologyInternal medicineAnimal sciencePathologyBiochemistryEcology

Abstract

fetched live from OpenAlex

Plasma neurofilament light chain (pNfL) concentration is a biomarker for neuroaxonal injury and degeneration and can be used to monitor response to treatment. Spontaneous canine neurodegenerative diseases are a valuable comparative resource for understanding similar human conditions and as large animal treatment models. The features of pNfL concentration in healthy dogs is not well established. We present data reporting basic pNfL concentration trends in the Labrador Retriever breed. Fifty-five Labrador Retrievers were enrolled. pNfL concentration was measured and correlated to age, sex, neuter status, height, weight, body mass index, and coat color. We found increased pNfL with age (P < 0.0001), shorter stature (P = 0.009) and decreased body weight (P < 0.001). These are similar to findings reported in humans. pNfL concentration did not correlate with sex, BMI or coat color. This data further supports findings that pNfL increase with age in a canine population but highlights a need to consider weight and height when determining normal pNfL concentration in canine populations.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.224
Teacher spread0.203 · 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 teacher head, 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

Citations17
Published2020
Admission routes1
Has abstractyes

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