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The Role of Positron Emission Tomography in the Diagnosis of Alzheimer's Disease

2004· letter· en· W1543377754 on OpenAlexaboutno aff
Jeffrey L. Cummings, D.H.S. Silverman, Gary W. Small, Michael E. Phelps

Bibliographic record

VenueJournal of the American Geriatrics Society · 2004
Typeletter
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsMedicineDementiaPositron emission tomographyMedical diagnosisAppropriate Use CriteriaMEDLINECost–benefit analysisMedical physicsDiseaseIntensive care medicineNuclear medicineRadiologyPathology

Abstract

fetched live from OpenAlex

To the Editor: We would like to address several issues raised by a recent article by Gill et al.1 purporting to review the evidence-based literature regarding the potential value of positron emission tomography (PET) in the diagnosis of dementia. It concludes that there is little evidence to support the integration of PET in the clinical evaluation of patients with suspected or established dementia. Several features of this article and the data reviewed challenge this conclusion. First, the article presents itself as a cost-benefit analysis of PET in the diagnosis of Alzheimer's disease (AD), but no cost-benefit or economic analysis was performed beyond stating the cost of these scans in Ontario, Canada. Such analyses have been conducted using established cost-benefit methods based on imaging and other costs and accounting for such advantages as early introduction of therapy, deferral of nursing home placement, and reduction in use of unnecessary testing. A formally conducted cost-benefit analysis demonstrated that PET is, under the most conservative analysis, at least cost neutral and, under more-realistic conditions, cost advantageous when added to the dementia evaluation.2 Second, the authors stated that they conducted a systematic review of the peer-reviewed literature from Medline from 1975 to 2001, but they did not identify the largest single-institutional study examining the relationship between PET-based and autopsy-based diagnoses, published in 2000.3 In addition, they excluded from their main analysis the largest multicenter study to examine this relationship,4 although its publication fell within the specified time, and they note that this latter article had several advantages over the articles examined. This article demonstrated 94% sensitivity and 73% specificity for PET in the diagnosis of AD, comparable with or better than most clinical diagnostic studies. Third, the authors do not provide criteria for deciding how PET was judged to be beneficial. They conducted no critical assessment or comparisons of sensitivity, specificity, or accuracy measures for clinical diagnoses or PET-based diagnoses. Moreover, they included only papers in their main analysis that had as their primary criterion standard the clinical diagnosis of AD, excluding those papers that used the more definitive standard of autopsy-confirmed diagnosis.3,4 There was consequently no way their analysis could demonstrate any incremental value of PET over clinical diagnosis by the way they constructed and performed their evaluation. Fourth, the authors repeatedly assert that clinical diagnosis of probable AD is straightforward and accurate in up to 90% of cases, thereby seeming to obviate a priori the need for neuroimaging. They identify only one study in support of this claim,5 a paper that the American Academy of Neurology (AAN) recently identified as having Class II quality of evidence. The paper showed that, to achieve a sensitivity of 90% (as occurs with PET), clinical specificity fell to below 40%. Three papers that the AAN rated as having Class I quality of evidence demonstrated a mean accuracy rate of clinical diagnosis of less than 70%. Fifth, Table 3 of the Gill et al.1 article lists the 16 articles or publications included in their review. Of these 16, five were published before the advent of any Food and Drug Administration–approved therapy for AD, and five more were published in 1993 and 1994, when tacrine, a little-used compound, was the only available treatment. Thus, 10 of the 16 articles precede the contemporary era of pharmacotherapeutics in the management of AD. This is important because an early and accurate diagnosis becomes more urgent once therapy is widely available. Sixth, the two Class A or B articles identified by the authors and published in 1996 (the only ones in the current era of neurotherapeutics for AD) were supportive of the use of PET. One study6 found high inter- and intraobserver agreement in PET interpretation of patients with probable AD, possible AD, mild cognitive impairment, and normal controls. Another study7 found that three-dimensional stereotactic surface projections improved sensitivity and specificity. Thus, the two articles published more recently and using more-modern PET scanners support the use of PET in the diagnosis of AD. Seventh, factual errors are present in this article. On four occasions in the article, the authors state that patients diagnosed with probable AD have advanced disease when the diagnosis is easiest and therefore PET adds little to resolve diagnostic challenges. It is untrue that probable AD is necessarily advanced. Probable AD8 refers to patients who meet research criteria for AD. These criteria can be applied as soon as the patient has impairment in memory and in at least one other cognitive domain causing disability. Patients may have Clinical Dementia Rating scale scores as low as 0.5 and still meet criteria for probable AD. Diagnosis of patients at this stage of dementia is a challenge because all dementias necessarily go through mild stages of severity before reaching more-severe and more diagnostically definitive stages. A large multicenter study4 showed the accuracy of PET to be the same at mild and moderate stages of AD. Eighth, the principal challenge in the recognition, diagnosis, and treatment of AD is the lack of recognition of patients by primary care practitioners who are most likely to encounter them in early stages of the disease. Practice reviews show that 97% of patients with early dementia go undiagnosed and as many as 50% of patients with moderate to severe dementia receive no diagnosis.9 Thus, the relatively similar sensitivity and specificities reported by some academic medical centers resulting from the rigorous application of research diagnostic criteria and those of PET are irrelevant to most routine clinical practices. Primary care practitioners fail to address the important issue of cognitive decline in their patients in part because of the absence of an available diagnostic test with which to confirm their opinion. PET should not replace a thorough clinical assessment but can add important and accurate positive evidence to the diagnosis based on traditional evaluations. In summary, the absence of a cost-benefit analysis, the lack of definitions for PET utility, the truncated literature review, the emphasis on out-of-date information, the factual misstatements, and the ignoring of the potential benefit of PET to those who are most likely to benefit from its availability undermine the conclusions of this article. The available literature supports the use of PET in the assessment of dementia, and we recommend that PET be integrated into the diagnostic approach to dementia.10

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 imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.098
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0060.001
Research integrity0.0150.017
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.010
GPT teacher head0.286
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations7
Published2004
Admission routes1
Has abstractyes

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