MétaCan
Menu
Back to cohort
Record W2756015345 · doi:10.1515/tnsci-2017-0014

The essentials of a global index for cognitive function

2017· article· en· W2756015345 on OpenAlexaff
Joseph M. Antony, Ian C.G. Weaver, Matthew Rueffer, Najla Guthrie, Malkanthi Evans

Bibliographic record

VenueTranslational Neuroscience · 2017
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsRobarts Clinical TrialsDalhousie University
Fundersnot available
KeywordsIndex (typography)Function (biology)CognitionPsychologyMedicineComputer scienceNeuroscienceWorld Wide WebBiology

Abstract

fetched live from OpenAlex

Cognition is comprised of the faculties: perception, creativity, intuition, and ratiocination. Optimal levels of cognition are needed for independent functioning and balanced living. With an aging population that continues to grow, dietary supplements that tilt the balance towards maintenance of cognition are being marketed for vulnerable populations facing these challenges. Randomized clinical trials provide the causal inference necessary to define the efficacy of emerging nutraceuticals. Cognition testing, in particular, requires a battery of tests that encompass all brain regions involved in cognition so as to provide endpoints necessary for product validation. The lack of well controlled studies for comparison analyses, limited sample sizes, ambiguous dosages, and poor cognitive measures result in data that cannot be compared across studies to determine the efficacy of supplements claiming to enhance cognition. Clinical trials for the nutraceutical industry should consider the multifaceted nature of supplements, where clinical endpoints must be comprehensive while remaining feasible. Combining endpoints of cognition with physiological biomarkers of immunity and metabolism to arrive at a global index for cognitive health may be necessary for claim substantiation in order to fully justify and scientifically validate improvements in cognitive health. The issues and needs of a global index will be discussed here.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.051
GPT teacher head0.355
Teacher spread0.304 · 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 designObservational
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

Citations16
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueTranslational NeuroscienceSame topicDiet and metabolism studiesFrench-language works237,207