Quantitative Pupillometry Predicts Neurologic Deterioration in Patients with Large Middle Cerebral Artery Stroke
Bibliographic record
Abstract
OBJECTIVE: This study assesses whether longitudinal quantitative pupillometry predicts neurological deterioration after large middle cerebral artery (MCA) stroke and determines how early changes are detectable. METHODS: This prospective, single-center observational cohort study included patients with large MCA stroke admitted to Boston Medical Center's intensive care unit (2019-2024). Associations between time-to-neurologic deterioration and quantitative pupillometry, including Neurological Pupil Index (NPi), were assessed using Cox proportional hazards models with time-dependent covariates adjusted for age, sex, and Alberta Stroke Program Early CT Score. Models using dilation velocity were compared with partial likelihood ratio tests. Pupillometric changes over 2-h intervals in the 12 h preceding deterioration were analyzed with linear mixed-effects modeling and Tukey's test. Matched referents (age, sex, stroke side, follow-up duration) were used for comparison. Optimal thresholds were identified using the Youden Index. RESULTS: Among 71 patients (mean age 66.5 years; 59.2% women), 32 (45.1%) experienced deterioration. A 1-unit decrease in NPi was associated with a higher hazard of deterioration (hazard ratio 2.46; 95% confidence interval 1.68-3.61). Dilation velocity improved model performance compared to NPi alone. NPi was significantly lower at 0-2 h (3.81 vs. 4.38, p = 0.001) and 2-4 h (3.71 vs. 4.38, p < 0.001) before deterioration compared to 10-12 h prior. Optimal thresholds were 4.01 for NPi, 0.49 mm/s for dilation velocity, and -0.15 change in NPi over 12 h. INTERPRETATION: Quantitative pupillometry predicts neurological deterioration in MCA stroke, with declines detectable up to 12 h prior. Dilation velocity shows promise as a novel biomarker. ANN NEUROL 2025;97:930-941.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".