Diagnostic performance of light reflex pupillometry in Alzheimer's disease
Bibliographic record
Abstract
Easily applied diagnostic tools such as digital biomarkers for Alzheimer's disease (AD) are urgently needed due to the recent approval of disease-modifying therapies. We aimed to determine the diagnostic performance of hand-held, quantitative light reflex pupillometry (qLRP) in patients with AD in a proof-of-concept, cross-sectional study. Participants underwent qLRP at a university memory clinic from August 2022 to October 2023. We fitted multivariable logistic regression models with qLRP, sex, and age as predictors evaluated with area under the receiver operating characteristics curve (AUROC). In total, 107 patients with AD, 44 patients with mixed AD and vascular cognitive dysfunction (VCD), 53 patients with dementia with Lewy bodies (DLB), and 50 healthy controls (HCs) were included. Our diagnostic models showed similar discriminatory ability (AUROC range 0.74-0.81) when distinguishing patients with AD from HCs and other dementias. The qLRP seems promising as a bedside digital biomarker to aid in diagnosing AD. Highlights: We demonstrated the diagnostic performance of qLRP in Alzheimer's disease.The diagnostic models were robust in sensitivity analyses.qLRP may assist in the bedside diagnostic evaluation of Alzheimer's disease.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".