Saccade Tasks: A Noninvasive Approach for Predicting Postoperative Delirium in Elderly Arthroplasty Patients
Bibliographic record
Abstract
BACKGROUND: Postoperative delirium (POD) is a prevalent complication in elderly surgical patients. It is associated with long-term cognitive impairment and increased dementia risk. However, reliable tools to predict POD are currently lacking. METHODS: The study enrolled 316 arthroplasty patients (aged 65 yr or older) in this study. Preoperative assessments comprised neuropsychological tests ( i.e. , Mini-Mental State Examination [MMSE] and Montreal Cognitive Assessment [MoCA]), molecular biomarkers of serum/cerebrospinal fluid, and saccadic tasks. POD was diagnosed by expert persons based on the Confusion Assessment Method test. The effectiveness of abovementioned three types of assessments in predicting the occurrence of POD were compared. RESULTS: The incidence of POD was 8.2% (26 of 316). The MMSE and MoCA scales, serum neurofilament light chain levels, and five saccadic parameters values (reaction time, primary saccade error, saccadic gains in pro-saccades; peak velocity in anti-saccades and memory-guided saccades) differed significantly ( P < 0.05) between POD and non-POD participants. The logistic regression classifier model revealed higher predictive accuracy when using saccadic parameters (area under the receiver operating characteristic curve [AUROC], 0.81; 95% CI, 0.70 to 0.92) than when using MMSE and MoCA scores (AUROC, 0.64; 95% CI, 0.53 to 0.76) or neurofilament light chain levels (AUROC, 0.61; 95% CI, 0.50 to 0.72). The multilayer perceptron machine learning classifier model further increased the accuracy (AUROC, 0.89; 95% CI, 0.82 to 0.94) by using saccadic parameters to predict POD occurrence. CONCLUSIONS: Saccadic parameters exhibited higher accuracy in predicting the occurrence of POD than MMSE and MoCA scores and molecular test results. Therefore, saccadic parameters may serve as a complementary behavioral biomarker for predicting the occurrence of POD in elderly arthroplasty patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".