P1‐179: Glucose metabolism of subjective cognitive impairment (SCI) analyzed by a new statistical parametric z‐score mapping (Zsmap)
Bibliographic record
Abstract
Subjective cognitive impairment (SCI) is receiving increasing attention as a pre-mild cognitive impairment condition in the course of the clinical manifestation of Alzheimer disease (Jessen et al. 2014, Perrotin et al. 2012). The purpose of the study was to evaluate glucose metabolism for the subjects with SCI using FDG-PET and a new voxel-based parametric image analysis. A total of 38 subjects with SCI (20 men; 18 women, mean age; 64 ± 11 y.o.) were examined using FDG-PET, MRI and several neuropsychological tests such as MMSE, MoCA, CASIS, GDS, everyday memory checklist based on Wilson's list and the test of awareness of memory deficits. The unawareness of memory deficits was defined as the difference of score between subject own rating and caregiver's rating. FDG-PET images were analyzed with a statistical parametric Z-score mapping based on SPM algorithms (Zsmap). In brief, images were spatially normalized, smoothed using symmetrical template, and compared with the mean and standard deviation of PET images of total 98 age–matched control subjects using voxel-by-voxel Z-score analysis. The regional glucose metabolism was evaluated by mean values of Z-scores, the values of which were superimposed on the standardized MRI template with a color scale and measured with Talairach atlas. The characteristic of the method was equipped with hippocampal slices and axial images were parallel to the orbito-meatal line because of well anatomical identification. Glucose metabolism was significantly decreased in the anterior cingulate, bilateral insular cortices and right anterior of temporal cortex. The reduction in the cingulate was independent of ages and also related to unawareness of memory impairment. The metabolism in the cingulate was decreased for the subjects within normal range of GDS score, however, abnormal scores were related to severity and wider regional reduction of glucose metabolism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".