Different criteria modify Alzheimer’s disease diagnosis and prognosis
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) has recently been reconceptualized into a dual entity, a biological disease, and a clinical syndrome. Following these advances, the National Institute on Aging – Alzheimer’s Association (NIA‐AA) and the International Working Group (IWG) released four updated diagnostic guidelines for AD diagnosis: NIA‐AA 2011, IWG 2016, NIA‐AA 2018, and IWG 2021. Although many studies still rely solely on a clinical diagnosis of AD, and a consensus on diagnostic criteria is still lacking. Here, we aim to elucidate how the aforementioned AD guidelines affect the individuals’ diagnostic label. Method Clinical, demographic and biomarker data were extracted from ADNI (n = 1215), and used to build classification algorithms according to diagnostic criteria from NIA‐AA 2011, IWG 2016, NIA‐AA 2018, and IWG 2021. Subjects then were labeled as Not AD (NA), At Risk of Developing AD (AR), or AD. Results were compared according to cognitive stage, cognitively unimpaired (CU) or cognitively impaired (CI), and to AD biomarkers profile. Kaplan‐Meier curves were used to evaluate conversion to CI in CU individuals using each diagnostic guideline. All analyses were performed in R (v 4.0.3). Result Individuals were diagnosed using criteria from the NIA‐AA 2011 [NA = 36.8%, AR = 23.2%, AD = 40%], IWG 2016 [NA = 37.9%, AR = 28.8%, AD = 33.3%], NIA‐AA 2018 [NA = 40.5%, AR = 11.1%, AD = 48.4%], and the IWG 2021 [NA = 51.3%, AR = 8.7%, AD = 40%] (Figure 1). Diagnoses were discordant between criteria in 39.8% of the individuals, with 69.4% of those being in the A+T‐ or A‐T+ biomarker groups. Conversion to CI of CU individuals AR compared to NA were calculated for the NIA‐AA 2011 [log‐rank 0,001], IWG 2016 [log‐rank 0,000], NIA‐AA 2018 [log‐rank 0,24], and IWG 2021 [log‐rank 0,000]. Conclusion Our findings indicated that almost 40% of the studied population presented discordant diagnoses using different AD criteria. Most of these had only one positive biomarker (amyloid or tau). All guidelines except for the NIA‐AA 2018 managed to differentiate asymptomatic individuals at risk of developing cognitive impairment. Our findings have important implications in clinical practice and the design of future clinical trials.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".