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Record W4309365772 · doi:10.2967/jnumed.122.264434

The Association of Age-Related and Off-Target Retention with Longitudinal Quantification of [ <sup>18</sup> F]MK6240 Tau PET in Target Regions

2022· article· en· W4309365772 on OpenAlexafffund
Cécile Tissot, Stijn Servaes, Firoza Z Lussier, João Pedro Ferrari‐Souza, Joseph Therriault, Pâmela C.L. Ferreira, Gleb Bezgin, Bruna Bellaver, Douglas Teixeira Leffa, Sulantha Mathotaarachchi, Mira Chamoun, Jenna Stevenson, Nesrine Rahmouni, Min Su Kang, Vanessa Pallen, Nina Margherita-Poltronetti, Jaime Fernandez‐Arias, Andréa Lessa Benedet, Eduardo R. Zimmer, Jean‐Paul Soucy, Dana Tudorascu, Annie Cohen, Madeleine Sharp, Serge Gauthier, Gassan Massarweh, Brian J. Lopresti, William E. Klunk, Suzanne L. Baker, Victor L. Villemagne, Pedro Rosa‐Neto, Tharick A. Pascoal

Bibliographic record

VenueJournal of Nuclear Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsMontreal Neurological Institute and HospitalDouglas Mental Health University InstituteMcGill UniversityArtificial Intelligence in Medicine (Canada)Ontario Brain InstituteSunnybrook Health Science Centre
FundersNational Institute on AgingCerveau TechnologiesWeston Brain InstituteMcGill UniversityNational Institutes of HealthAlzheimer's Association
KeywordsAssociation (psychology)Nuclear medicinePhysicsPsychologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Inferior CG was similar across diagnostic groups cross-sectionally and stable over time, thus deemed a suitable reference region for quantification. Despite not being visually perceptible, [ 18 F]MK6240 has age-related retention in subcortical regions, in much lower magnitude but topographically co-localized with significant off-target signal of the first-generation tau tracers. The lack of correlation between changes in age-related/meningeal and target retention suggests little influence of possible off-target signals on longitudinal tracer quantification.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.028
GPT teacher head0.291
Teacher spread0.263 · 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 designNot applicable
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

Citations25
Published2022
Admission routes2
Has abstractyes

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