Predictive Ability of a Serious Game to Identify Emergency Patients With Unrecognized Delirium
Bibliographic record
Abstract
OBJECTIVES: Recognition of delirium in the emergency department (ED) is poor. Our objectives were to assess: (1) the diagnostic accuracy of the Predicting Emergency department Delirium with an Interactive Computer Tablet (PrEDICT) "serious game" to identify older ED patients with delirium compared to clinical recognition and (2) the feasibility of the PrEDICT application compared to existing tests of attention. DESIGN: Prospective observational study. SETTING: ED of a Canadian tertiary care center. PARTICIPANTS: We included ED patients, aged 70 years and older, with a minimum 4-hour stay. We excluded anyone with critical illness, communication barriers, and visual impairment or those unable to use a computer tablet. None had prevalent delirium by ED clinicians' routine clinical assessment. MEASUREMENTS: Participants were asked to tap targets on a tablet at four difficulty levels. Time and accuracy were automatically recorded. Other measures included the Confusion Assessment Method, the Delirium Severity Index, the Digit Vigilance Test (DVT), and the Choice Reaction Test (CRT). RESULTS: We enrolled 203 patients. Their average age was 80.6 years, 49.8% were female, and their average ED length of stay was 15.9 hours. Sixteen subjects had clinically unrecognized delirium, and 14 of them completed the PrEDICT game (87.5%). We developed a threshold score with 100% sensitivity (95% confidence interval [CI] = 76.8%-100.0%) and 59.7% specificity (95% CI = 52.3%-66.6%) to identify patients with clinically unrecognized delirium. The area under the curve was 0.86 (95% CI = 0.77-0.94). Completion rates were 196/203 (96.6%) for the PrEDICT serious game compared to 128/203 (63.1%) for the CRT and 51/203 (25.1%) for the DVT. CONCLUSION: Older ED patients were able to use our serious game, including 87.5% of those with clinically unrecognized delirium. The PrEDICT application has potential to act as a sensitive screening tool to identify older ED patients with clinically unrecognized delirium. J Am Geriatr Soc 67:2370-2375, 2019.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
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".