From Traditional Typing to Intelligent Insights: A Narrative Review of Directions Toward Targeted Therapies in Delirium
Bibliographic record
Abstract
Delirium is a heterogeneous syndrome characterized by an acute change in level of consciousness that is associated with inattention and disorganized thinking. Delirium affects most critically ill patients and is associated with poor patient-oriented outcomes such as increased mortality, longer ICU and hospital length of stay, and worse long-term cognitive outcomes. The concept of delirium and its subtypes has existed since nearly the beginning of recorded medical literature, yet robust therapies have yet to be identified. Analogous to other critical illness syndromes, we suspect the lack of identified therapies stems from patient heterogeneity and prior subtyping efforts that do not capture the underlying etiology of delirium. The time has come to leverage machine learning approaches, such as supervised and unsupervised clustering, to identify clinical and pathophysiological distinct clusters of delirium that will likely respond differently to various interventions. We use sedation in the ICU as an example of how precision therapies can be applied to critically ill patients, highlighting the fact that while for some patients a sedative drug may cause delirium, in another cohort sedation is the specific treatment. Finally, we conclude with a proposition to move away from the term delirium, and rather focus on the treatable traits that may allow precision therapies to be tested.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".