Advancing specificity in delirium: The delirium subtyping initiative
Bibliographic record
Abstract
BACKGROUND: Delirium, a common syndrome with heterogeneous etiologies and clinical presentations, is associated with poor long-term outcomes. Recording and analyzing all delirium equally could be hindering the field's understanding of pathophysiology and identification of targeted treatments. Current delirium subtyping methods reflect clinically evident features but likely do not account for underlying biology. METHODS: The Delirium Subtyping Initiative (DSI) held three sessions with an international panel of 25 experts. RESULTS: Meeting participants suggest further characterization of delirium features to complement the existing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition Text Revision diagnostic criteria. These should span the range of delirium-spectrum syndromes and be measured consistently across studies. Clinical features should be recorded in conjunction with biospecimen collection, where feasible, in a standardized way, to determine temporal associations of biology coincident with clinical fluctuations. DISCUSSION: The DSI made recommendations spanning the breadth of delirium research including clinical features, study planning, data collection, and data analysis for characterization of candidate delirium subtypes. HIGHLIGHTS: Delirium features must be clearly defined, standardized, and operationalized. Large datasets incorporating both clinical and biomarker variables should be analyzed together. Delirium screening should incorporate communication and reasoning.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
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 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".