Optimization of automated quantification of 123I-IBZM uptake in the striatum applied to parkinsonism.
Bibliographic record
Abstract
UNLABELLED: Evaluation of therapies for parkinsonism by dopamine receptor SPECT requires a reproducible, optimized quantitation technique. This study presents a new, objective, automated technique for semiquantitative analysis of dopamine receptor density, as applied to the differential diagnosis of parkinsonism. METHODS: Dopamine receptor density measured by 123I-iodobenzamide (IBZM) SPECT was retrospectively analyzed in nonidiopathic parkinsonism (NIPS), in Parkinson's disease (PD), and in healthy volunteers (n = 19, 38, and 13, respectively). A mean template was created from coregistered control studies. Registration errors were assessed using studies with simulated binding deficits. Patient studies were registered to the mean template, and striatal binding was calculated from a corresponding map of 3-dimensional regions of interest (ROIs). The striatal binding ratio and deficits determined by voxelwise comparison with the normal template were investigated and tested with various 3-dimensional ROI sizes and positions. Separation of patient groups was determined by tscore after automatically processing all studies. Results were compared with manual ROI analyses. RESULTS: The automatic method was completely reproducible in 64 of 70 cases. The best diagnostic discriminator was the minimum binding ratio of the 2 striatal nuclei, with the following values: NIPS, 1.33+/-0.13; PD, 1.50+/-0.12; healthy volunteers, 1.49+/-0.08 (+/-SD). The deficit size from voxelwise analysis was: NIPS, 20.5+/-8.2 mL; PD, 9.5+/-8.3; healthy volunteers, 8.9+/-6.0 (+/-SD). The accuracy, measured by receiver operating characteristic areas, was 0.85+/-0.05, 0.77+/-0.06, and 0.80+/-0.06 (+/-SE) for the optimal predictor (automated) and 2 blinded observers (manual), respectively. CONCLUSION: A new 3-dimensional, automated technique has been developed to semiquantitate receptor density that dramatically improves reproducibility. The optimal diagnostic discriminator of parkinsonism determined by the automatic technique has good accuracy compared with the manual technique.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".