Evaluating the effect of point-sampling on univariate point and interval forecasting of cerebral physiologic signals using ARIMA modeling in acute traumatic neural injury
Bibliographic record
Abstract
High-resolution physiological signals, such as intracranial pressure (ICP) and regional cerebral oxygen saturation (rSO 2 ), are critical for managing traumatic brain injury (TBI) by enabling continuous monitoring of cerebral autoregulation and vascular reactivity. These signals provide essential insights into brain perfusion dynamics, supporting timely clinical interventions. However, the high temporal resolution of these data introduces challenges in real-time use, integration into predictive models, and computational efficiency. Consequently, resolution reduction techniques are essential for simplifying the data while retaining critical features necessary for accurate prediction and modeling. Using the Multi-omic Analytics and Integrative Neuroinformatics in the HUman Brain (MAIN-HUB) Lab database, high-frequency cerebral physiologic dataset, we aimed to evaluate the effects of point-sampling resolution reduction on point and interval predictions using the autoregressive integrated moving average (ARIMA) model for both raw signals and derived indices. Temporal resolution was reduced by selecting the first value within non-overlapping intervals, ranging from 1-min (min) to 12-h windows. A total of 125 patient data was analyzed across various temporal resolutions. The results indicated that ARIMA models performed well at higher resolutions (e.g., 1-min), capturing short-term physiological dynamics with lower errors. However, as resolution decreased, errors and variability increased, particularly for signals like mean arterial pressure (MAP) and cerebral perfusion pressure (CPP), which exhibit rapid or complex physiological changes. The findings underscore the need to carefully balance temporal resolution, model performance, and computational efficiency, especially when dealing with high-frequency physiological data in clinical settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".