Multivariate Analysis in Clinical Monitoring: Detection of Intraoperative Hemorrhage and Light Anesthesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The number of vital sign variables measured during a typical surgery is beyond the simultaneous surveillance capabilities of most experienced clinicians. Most intraoperative events cause trend changes in multiple variables, and many clinical events can only be detected by investigating the inter-relationship between the direction and amplitude of these trend changes in the whole measurement array. We have compared the techniques of principal component analysis (PCA) and factor analysis (FA) in extracting latent variables to represent the underlying physiological mechanism. The detection performance of each method was tested on three simulated cases of intraoperative hemorrhage and a case of variation in depth of anesthesia. The results show that although the detection schemes based on PCA and FA both reduce dimensionality and detect changes in the variance, the FA-based method performs better in detecting subtle changes in the correlation structure.
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.
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