Time-Series autocorrelative structure of cerebrovascular reactivity metrics in severe neural injury: An evaluation of the impact of data resolution
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.
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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".