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Record W3201385250 · doi:10.1007/s11682-021-00543-3

Tipping the scales: how clinical assessment shapes the neural correlates of Parkinson’s disease mild cognitive impairment

2021· article· en· W3201385250 on OpenAlexaboutno aff
Ignacio Aracil‐Bolaños, Frederic Sampedro, Juan Marín‐Lahoz, Andrea Horta‐Barba, Saül Martínez‐Horta, José María Gónzalez‐de‐Echávarri, Jesús Pérez‐Pérez, Helena Bejr‐Kasem, Berta Pascual‐Sedano, M. Botí, Antònia Campolongo, Cristina Izquierdo, Alexandre Gironell, Beatriz Gómez‐Ansón, Jaime Kulisevsky, Javier Pagonabarraga

Bibliographic record

VenueBrain Imaging and Behavior · 2021
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIIFundació la Marató de TV3
KeywordsDefault mode networkMontreal Cognitive AssessmentPrecuneusPsychologyNeuropsychologyBetweenness centralityNeuroimagingVoxel-based morphometryParkinson's diseaseCognitionAudiologyNeuroscienceMedicineInternal medicineDiseaseMagnetic resonance imagingCognitive impairmentWhite matterRadiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.346
Teacher spread0.312 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractno

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