Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence
Bibliographic record
Abstract
The paradigm of precision medicine often focuses on novel biomarkers, but other types of data directly applicable to patient care could individualize treatment. The response to a cardiovascular and pulmonary intervention is important to critical care providers because it may determine the need and intensity of organ support. Using patient response to predict benefit (or no effect or harm) from an intervention could streamline care better for common syndromes of critical illness such as shock and ARDS, but the necessary data collection and analysis often are complex. Augmented intelligence technologies could assist with this kind of phenotyping because they can analyze and interpret complex multimodal data. In this narrative review, we summarize how augmented intelligence has been used to phenotype responses to diagnostic and therapeutic interventions in cardiovascular and pulmonary failure. We also discuss opportunities for future research that could make this precision approach useful in the clinical environment.
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How this classification was reachedexpand
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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".