PREDICT - PREDICTING EMERGENCY DEPARTMENT INCIDENT DELIRIUM WITH AN INTERACTIVE COMPUTER TABLET
Bibliographic record
Abstract
Background: Delirium is common and potentially lethal. Unfortunately, delirium recognition is poor- 75% of Emergency Department (ED) cases are missed. Objectives: We previously developed a “serious game” using participants’ game performance to evaluate delirium risk. Our goal is to validate this algorithm against the delirium severity index (DSI). Methods: This is multi-center prospective observational trial included an English and French version. We included patients ≥65 years of age and excluded those with critically illness, pain ≥6/10, or communication difficulties. Consenting participants played the serious game and then had DSI and Montreal Cognitive Assessment (MOCA) assessed by trained research assistants. We used logistic regression to assess the relationship between a game score based on > 800 data points and DSI ≥4, controlling for age and sex. Results: We enrolled 306 participants from 3 Canada provinces -92.3% of completed the game. Their average age was 75.9 and 48.7% were women. Their median MOCA score was 23. There were 24/306 patients with a DSI ≥4 out of a possible 21 points. Their mean game score was 0.70 (95% CI 0.63–0.78) vs. 0.61 (95% CI 0.60–0.63) for participants with a DSI<4.The odds of having a DSI≥4 increased 1.89 for each 0.1 increase in game score (95% CI for OR = 1.4–2.7, p=<0.001). Conclusions: The odds of having a DSI≥ 4 significantly increased with game scores. The vast majority of older ED patients could use our game. Given current poor clinical delirium recognition, our “serious game” may help improve delirium recognition.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".