The use of SPECT in the diagnosis of Parkinson's disease.
Bibliographic record
Abstract
This article looks briefly at the latest efforts to develop an objective diagnostic marker for Parkinson's disease on single-photon emission computed tomography (SPECT). Traditionally, the diagnosis of idiopathic Parkinson's disease has been based on clinical criteria. However, these predict the pathologic diagnosis in only 80% of patients suspected of having the disease. Since a correct diagnosis is essential for prognosis, effective treatment and research, the search has continued for objective markers. The latest developments in nuclear medicine have come the closest in making such a marker clinically available. These developments are based on SPECT and positron-emission tomographic imaging of the basal ganglia using specific radio-labelled dopaminergic-receptor tracers. SPECT radiotracers target either the pre- or postsynaptic component of the dopaminergic system in the basal ganglia. These techniques show great promise in the early diagnosis of PD as well as in measuring its progression.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".