Systematic review of the body of evidence for the use of biomarkers in the diagnosis of dementia
Bibliographic record
Abstract
BACKGROUND: Although recent diagnostic criteria for Alzheimer's disease propose the use of biomarkers, validation of these biomarkers by diagnostic test accuracy studies is a necessary first step, followed by the synthesis of the evidence from these studies in systematic reviews and meta-analyses. The quality of the resulting evidence depends on the number and size of the primary studies, their quality, and the adequacy of their reporting. This systematic review assesses the weight and quality of the evidence available from primary diagnostic test accuracy studies. METHODS: A MEDLINE search was performed in August 2011 to identify all potentially relevant publications relating to the biomarkers β-amyloid, tau, positron emission tomography ((18)F-fluorodeoxyglucose or ligands for amyloid), or magnetic resonance imaging (MRI). The reporting and methodology were assessed using the Standards for Reporting of Diagnostic Accuracy and Quality Assessment of Diagnostic Accuracy Studies assessment tools, respectively. Because clinical progression to dementia is the most commonly used reference standard, this review focuses on participants with objective cognitive impairment but no dementia at baseline. RESULTS: Of the 19,104 published references identified by the search, 142 longitudinal studies relating to the biomarkers of interest were identified, which included subjects who had objective cognitive impairment but no dementia at baseline. The highest number of studies (n = 70) and of participants (n = 4722) related to structural MRI. MRI also yielded the highest number of studies with extractable data for meta-analysis (n = 32 [46% of all structural MRI studies]), followed by cerebrospinal fluid tau (n = 24 [73%]). There were few studies on positron emission tomography ligands for amyloid having suitable data for meta-analysis (n = 4). There was considerable variation across studies in reporting outcomes, methods of blinding and selection, means of accounting for indeterminate or missing values, the interval between the test and assessments, and the determination of test thresholds. CONCLUSIONS: The body of evidence for biomarkers is not large and is variable across the different types of biomarkers. Important information is missing from many study reports, highlighting the need for standardization of methodology and reporting to improve the rigor of biomarker validation.
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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 | 0.030 | 0.131 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".