The Value of Positron Emission Tomography in the Clinical Evaluation of Dementia
Bibliographic record
Abstract
Positron emission tomography (PET) has been promoted as a means of improving the diagnosis of Alzheimer's disease (AD), but the evidence to support its incremental value is unclear. To assess the evidence regarding the use of PET in the clinical evaluation of AD, a systematic review of the English-language literature indexed in MEDLINE (1975-January 2001), the Cochrane Library (issue 4, 2000), and health technology assessment (HTA) reports was conducted. Articles identified by this review process were graded for methodological and reporting quality using a standardized grading scheme. Sixteen original articles and seven HTA reports were identified. In general, the articles addressed: using PET to differentiate AD from normal aging or non-Alzheimer's dementias, PET imaging compared with single positron emission computed tomography imaging, using PET to predict the progression of dementia, and agreement and reliability in the interpretation of PET images. Serious problems with study design and methodology in all articles were identified. Previous HTA reports have generally recommended that PET not be used in the clinical evaluation of dementia. In conclusion, there is little evidence to support the addition of PET to the routine clinical evaluation of patients with suspected or established dementia. Suggestions for future research in this area are offered.
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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 | 0.080 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".