Bibliographic record
Abstract
The purpose of functional imaging in clinical practice is to increase diagnostic accuracy when differentiating between dementia disorders. The techniques most often used are imaging of regional cerebral blood flow (rCBF), using single photon emission computed tomography (SPECT), and imaging of glucose metabolism using positron emission tomography (PET). rCBF assessed with SPECT has been used to study vascular reactivity and to evaluate the effect of dementia treatment. It has also been studied in CADASIL, a hereditary form of vascular dementia (VaD). PET shows the same disease pattern as SPECT when ligands are used for investigating rCBF. It is also used to study regional glucose metabolism using Fluoro-Deoxy-D-Glucose (FDG), oxygen metabolic extraction rate and cerebral oxygen metabolic rate. According to the meta review by Dougall and his group, it is clear that the clinical usefulness of SPECT in differentiating VaD from Alzheimer's disease is limited.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".