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Effect of Dopamine Loss and the Metabolite 3- <i>O</i> -Methyl-[ <sup>18</sup> F]Fluoro-dopa on the Relation between the <sup>18</sup> F-Fluorodopa Tissue Input Uptake Rate Constant K <sub>occ</sub> and the [ <sup>18</sup> F]Fluorodopa Plasma Input Uptake Rate Constant K <sub>i</sub>

2003· article· en· W2063723084 on OpenAlexaff
Vesna Sossi, J. E. Holden, Raúl de la Fuente‐Fernández, T.J. Ruth, A. Jon Stoessl

Bibliographic record

VenueJournal of Cerebral Blood Flow & Metabolism · 2003
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsTRIUMFUniversity of British Columbia
Fundersnot available
KeywordsMetaboliteDopamineDopaminergicPositron emission tomographyReaction rate constantTRACERChemistryParkinson's diseaseDiseasePhysicsInternal medicineNuclear medicineBiochemistryMedicineKinetics

Abstract

fetched live from OpenAlex

Parkinson disease is characterized by the loss of dopaminergic neurons, thus decreasing the system's ability to produce and store dopamine (DA). Such ability is often investigated using 18F-fluorodopa (FD) positron emission tomography. A commonly used model to investigate the DA synthesis and storage rate is the modified Patlak graphical approach. This approach allows for both plasma and tissue input functions, yielding the respective uptake rate constants K(i) and K(occ). This method requires the presence of an irreversible compartment and the absence of any nontrapped tracer metabolite. In the case of K(occ), this last assumption is violated by the presence of the FD metabolite 3-O-methyl-[18F]fluoro-dopa (3OMFD), which makes the K(occ) evaluation susceptible to a downward bias. It was found that both K(i) and K(occ) are influenced by DA loss and thus are not pure measures of DA synthesis and storage. In the case of K(occ), the presence of 3OMFD exacerbates the effect of DA egress, thus introducing a disease-dependent bias in the K(occ) determination. These findings imply that K(i) and K(occ) provide different assessments of disease severity and that, as disease progresses, K(i) and especially K(occ) become more related to DA storage capacity and less to the DA synthesis rate.

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)0.022
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.005
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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

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

Citations36
Published2003
Admission routes1
Has abstractyes

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