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Record W3167140178 · doi:10.3389/fnrgo.2021.678541

Current Methodological Pitfalls and Caveats in the Assessment of Exercise-Induced Changes in Peripheral Brain-Derived Neurotrophic Factor: How Result Reproducibility Can Be Improved

2021· article· en· W3167140178 on OpenAlex
Chiara Nicolini, Aimee J. Nelson

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Neuroergonomics · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNeuroplasticityNeuroscienceNeurotrophic factorsBrain-derived neurotrophic factorPsychologyMediatorPeripheralPhysical medicine and rehabilitationMedicineInternal medicine

Abstract

fetched live from OpenAlex

Neural mechanisms, such as enhanced neuroplasticity within the motor system, underpin exercise-induced motor improvements. Being a key mediator of motor plasticity, brain-derived neurotrophic factor (BDNF) is likely to play an important role in mediating exercise positive effects on motor function. Difficulties in assessing brain BDNF levels in humans have drawn attention to quantification of blood BDNF and raise the question of whether peripheral BDNF contributes to exercise-related motor improvements. Methodological and non-methodological factors influence measurements of blood BDNF introducing a substantial variability that complicates result interpretation and leads to inconsistencies among studies. Here, we discuss methodology-related issues and approaches emerging from current findings to reduce variability and increase result reproducibility.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.124
GPT teacher head0.347
Teacher spread0.224 · 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