Time-Series autocorrelative structure of cerebrovascular reactivity metrics in severe neural injury: An evaluation of the impact of data resolution
Why this work is in the frame
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Bibliographic record
Abstract
Goal: Cerebrovascular reactivity (CVR) dysfunction is a contributor to secondary injury in traumatic brain injury (TBI). The issue with applying non-overlapping moving average filters to reduce temporal resolution of high resolution CVR data is that the autocorrelative structure is ignored. It violates the priors of linearity and raises concerns for the level of certainty for any reported models. The goal is to assess if there is a data resolution for cerebral physiology where Box-Jenkin’s time-series statistical structures can be ignored. The CVR indices were derived in varying temporal resolutions from 10-second to 1-day and each signals’ stationarity was assessed. By varying autoregressive order (1–10), integrative order (0–2), and moving average order (0–10), the autoregressive integrative moving average (ARIMA) models were fit to each index in varying temporal resolutions to obtain median optimal ARIMA models. A total of 100 patients were included with 3934.5 minutes of median recording duration. The stationarity analysis showed stationarity in 1st and 2nd order differenced data after temporal reduction. The median optimal ARIMA models for each combination of temporal resolution and CVR indices were found based on Akaike Information Criterion. Autocorrelative function (ACF) and partial ACF plots of residuals confirmed median optimal ARIMA model adequacy. For accurate predictions/trajectory forecasting, the autocorrelative structure needs to be accounted for in CVR data using an autocorrelative order of 8–10 for high frequency data and about 5 for low frequency data. Also, there is the need to understand such ARIMA structures in raw multi-modal cerebral physiology using multi-center high-resolution datasets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it