Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort
Bibliographic record
Abstract
Therapeutic management during the acute phase of traumatic brain injury (TBI) relies on continuous multimodal cerebral physiologic monitoring to detect and prevent secondary injury. These high-resolution data streams come from various invasive/non-invasive sensor technologies and challenge clinicians, as they are difficult to integrate into management algorithms and prognostic models. Data reduction techniques, like moving average filters, simplify data but may fail to address statistical autocorrelation and could introduce new properties, affecting model utility and interpretation. This study uses the CAnadian High-Resolution TBI (CAHR-TBI) dataset to examine the impact of temporal resolution changes (1 min to 24 h) on autoregressive integrated moving average (ARIMA) modeling for raw and derived cerebral physiologic signals. Stationarity tests indicated that the majority of the signals required first-order differencing to address persistent trends. A grid search identified optimal ARIMA parameters (p,d,q) for each signal and resolution. Subgroup analyses revealed population-specific differences in temporal structure, and small-scale forecasting using optimal parameters confirmed model adequacy. Variations in optimal structures across signals and patients highlight the importance of tailoring ARIMA models for precise interpretation and performance. Findings show that both raw and derived indices exhibit intrinsic ARIMA components regardless of resolution. Ignoring these features risks compromising the significance of models developed from such data. This underscores the need for careful resolution considerations in temporal modeling for TBI care.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".