Clinical Impact of a Second FDG-PET in Atypical/Unclear Dementia Syndromes
Bibliographic record
Abstract
Diagnosis of atypical/unclear dementia is often difficult and this delays treatment initiation. Several authors have shown that beyond standard dementia workup, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) reduces the number of unclear diagnoses, leads to earlier treatment, and has a beneficial impact on families. However, it is not uncommon that the FDG-PET findings are equivocal in this setting. For those cases, a repeat FDG-PET may clarify the diagnosis and prevent treatment delay. We retrospectively assessed the clinical impact of a repeat FDG-PET in 59 patients with atypical/unclear dementia syndromes and inconclusive initial FDG-PET. Changes in primary diagnosis, diagnostic confidence, and management following the second FDG-PET were examined. Conducting a second FDG-PET reduced the number of unclear diagnoses from 80% to 34% , led to diagnostic change in 24% of cases, and treatment modification in 22% of patients. Overall, the clinical impact was higher when initial diagnostic confidence was low and the second FDG-PET repeated ≥12 months after the first one. In tertiary care memory clinic settings, when diagnostic incertitude persists despite extensive evaluation and an equivocal FDG-PET, repeating the FDG-PET 12 months later can greatly clarify the diagnosis and improve management.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".