Imaging of dopamine transporters in Parkinson disease: a meta‐analysis of <sup>18</sup>F/<sup>123</sup>I‐FP‐CIT studies
Bibliographic record
Abstract
Abstract Objective 18F‐FP‐CIT and 123I‐FP‐CIT are widely used radiotracers in molecular imaging for Parkinson’s disease (PD) diagnosis. Compared with 123I‐FP‐CIT, 18F‐FP‐CIT has superior tracer kinetics. We aimed to conduct a meta‐analysis to assess the efficacy of using 18F‐FP‐CIT positron emission tomography (PET) and 123I‐FP‐CIT single‐photon emission computed tomography (SPECT) of dopamine transporters in patients with PD in order to provide evidence for clinical decision‐making. Methods We searched the PubMed, Embase, Wanfang Data, and China National Knowledge Infrastructure databases to identify the relevant studies from the time of inception of the databases to 30 April 2020. We identified six PET studies, including 779 patients with PD and 124 healthy controls, which met the inclusion criteria. Twenty‐seven SPECT studies with 1244 PD patients and 859 controls were also included in this meta‐analysis. Results Overall effect‐size analysis indicated that patients with PD showed significantly reduced 18F‐FP‐CIT uptake in three brain regions [caudate nucleus: standardized mean difference (SMD) = −1.71, Z = −3.31, P = 0.0009; anterior putamen: SMD = −3.71, Z = −6.26, P < 0.0001; and posterior putamen: SMD = −5.49, Z = −5.97, P < 0.0001]. Significant decreases of 123I‐FP‐CIT uptake were also observed in the caudate (SMD = −2.31, Z = −11.49, P < 0.0001) and putamen (SMD = −3.25, Z = −14.79, P < 0.0001). Interpretation In conclusion, our findings indicate that both 18F‐FP‐CIT PET and 123I‐FP‐CIT SPECT imaging of dopamine transporters can provide viable biomarkers for early PD diagnosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".