A PCA based feature reduction in intracranial hypertension analysis
Bibliographic record
Abstract
Traumatic brain injury (TBI) and its complications, including intracranial hypertension, are one of the leading causes of mortality. Many proposed algorithms have attempted to overcome the invasiveness of intracranial pressure monitoring with limited clinical applications. In medical practices, changes of intracranial hypertension are perceived manually, by clinical experts, via surgical placement of intraventricular catheters in the patient's skull. However, the method is invasive, time consuming and has poor reproducibility. In this paper, a principal component analysis has been conducted to perform non-correlated feature selection. An analysis has been performed on 31 TBI patients. The result of the analysis illustrates that two components can be extracted from routinely collected physiological signals of TBI patients. The finding is evaluated using Kaiser's Criterion, Scree test and parallel analysis. The predictivity of the components has been tested and the results show a promising accuracy with a mean absolute error of 0.025.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".