Decoding a Hidden Energy State Based on Marked Point Process Cortisol Secretory Events During Cardiac Surgery
Bibliographic record
Abstract
Cortisol is critical in regulating one's energy state in response to stressful events such as surgical procedures. Decoding a cortisol-related energy state during surgery can assist in managing one's overall health status under inflammation. In this study, we decode a hidden cortisol-related energy state from each patient's cortisol profile during coronary arterial bypass grafting surgery. In particular, we employ a Bayesian state estimation approach within an expectation-maximization framework and estimate the energy state from the observation vector, which consists of the inferred cortisol secretory events coupled with a reconstructed high frequency cortisol profile. This reconstructed cortisol profile has a one-minute resolution and is obtained by using the estimates from deconvolution of cortisol data sampled at every 10 minutes. We find a higher energy state within the post-surgery phase compared to the surgery phase for all the studied patients (10 patients), which may depict the decoder's reliability in manifesting clinically relevant information. Tracking the person-specific cortisol-related energy state during surgery could provide insights into intervention design procedures and treatment plans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".