Application of two‐graph receiver operating characteristics for defining intermediate amyloid‐beta CSF cutoffs
Bibliographic record
Abstract
Abstract Background The NIA‐AA 2018 Research Framework envisioned Alzheimer's disease (AD) as a biological construct, dichotomizing individuals into normal and abnormal for each biomarker category. Furthermore, it suggests that developing intermediate‐range cutoffs would aid in advancing AD research. However, few statistical methodologies have been proposed to define what would constitute an intermediate range for an AD biomarker. Here, we apply a three‐range method for defining cutoff in amyloid‐β (Aβ) cerebrospinal fluid (CSF) measurements to assess intermediate range's predictive value over clinical progression. Considering it is expected that a biomarker loses its predictive value in the perithreshold zone, we hypothesized that Aβ‐intermediate (AbInt) status of cognitively unimpaired (CU) would not be able to predict progression to symptomatic stages of AD. Method For defining intermediate‐range cutoffs with two‐graph receiver operating characteristics (TG‐ROC) method, 807 individuals with baseline [18F]AV45 Aβ‐PET and CSF Aβ1‐42 Elecsys® biomarkers were selected from the ADNI. Mean standardized uptake value ratio cutoff of 1.11 for [18F]AV45 was employed to stratify individuals into true‐positive (n=433) and true‐negative (n=374). Based on TG‐ROC cutoffs, 336 CU individuals with baseline CSF Aβ1‐42 Elecsys® were divided into Aβ‐, Aβint and Aβ+ and followed‐up for 6 years. Predictive values of baseline biomarker status for clinical progression were assessed using Cox proportional hazards models adjusted for age, gender, APOE and baseline MMSE. Result TG‐ROC analysis yielded cutoffs for Aβ‐ (>1131 pg/mL; n=199), AβInt (820.1 Int (HR=‐0.375; 95% CI 0.259‐1.826; p=0.452). Kaplan‐Meier survival curves illustrate these trajectories (Figure 2). Conclusion Our findings support the notion that biomarker measurements lose their ability to significantly predict cognitive decline within the perithreshold zone, emphasizing the need for further investigation of these individuals. As an alternative for operationalizing this matter of pivotal importance, we propose further investigation about the clinical utility of TG‐ROC, a well‐established method for defining three‐range cutoffs.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".