A Metabolic Imaging Severity Rating Scale for the Assessment of Cognitive Impairment
Bibliographic record
Abstract
PURPOSE: This study was designed to validate a simple scoring system for evaluating fluorodeoxyglucose (FDG) positron emission tomographic (PET) scans that can be used routinely in patients undergoing the clinical assessment of cognitive impairment. METHODS: The FDG-PET scans of 106 patients with cognitive impairment (65 with Alzheimer disease, 16 with frontal lobe dementia, and 25 atypical cases) were acquired using the PENN-PET scanner 40 minutes after the intravenous administration of 8 mCi FDG. Metabolic activity in various anatomic structures of the brain was scored using the following qualitative scale: 4 = normal; 3 = mildly decreased; 2 = moderately decreased; 1 = severely decreased; and 0 = no activity. Regions of interest were also placed over these regions to obtain a quantitative value. Two distinct scores were obtained. Values for visual and sensorimotor cortices, thalami, basal ganglia, and cerebellum comprised score I. Score II consisted of the values for the frontal, temporal, and parietal cortices. The qualitative metabolic imaging severity rating scale (MISRS) was compared with a quantitative MISRS (obtained from the region-of-interest analysis of the same structures). The MISRS was then compared with the results from the Mini-Mental Status Examination (MMSE) and the Dementia Severity Rating Score (DSRS). RESULTS: In all patients, the qualitative MISRS scores correlated significantly with the quantitative MISRS (r = 0.73, P < 0.0001). In all patients with cognitive impairment, the qualitative and quantitative MISRS scores correlated significantly with the DSRS and the MMSE (P < 0.001). In patients with Alzheimer disease, the qualitative and quantitative MISRS significantly correlated with the DSRS and MMSE. CONCLUSION: A simple and practical rating scale can be used to assess the severity of cognitive impairment in patients with different types of dementing illnesses.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".