Identifying Cognitive Impairment in the Acute Care Hospital Setting: Finding an Appropriate Screening Tool
Bibliographic record
Abstract
IMPORTANCE: Identifying cognitive impairment in adults in acute care is essential so that providers can address functional deficits and plan for safe discharge. Occupational therapy practitioners play an essential role in screening for, evaluating, and treating cognitive impairment. OBJECTIVE: To test and compare the psychometrics and feasibility of three cognitive screens and select the ideal screen for use in acute care. DESIGN: Prospective mixed methods. SETTING: Acute care hospital. PARTICIPANTS: Fifty adults. OUTCOMES AND MEASURES: We examined the interrater reliability, administration time, and usability of the Brief Cognitive Assessment Tool Short Form (BCAT-SF), the Activity Measure for Post-Acute Care "6-Clicks" Applied Cognitive Inpatient Short Form (AM-PAC ACISF), and the Montreal Cognitive Assessment (MoCA). We compared the construct validity, sensitivity, and specificity of the BCAT-SF and AM-PAC ACISF with those of the MoCA. RESULTS: Interrater reliability was good to excellent; ICCs were .98 for the MoCA, .97 for the BCAT-SF, and .86 for the AM-PAC ACISF. The BCAT-SF and the AM-PAC ACISF both had 100% sensitivity, and specificity was 74% for the BCAT-SF and 98% for the AM-PAC ACISF. The optimal cutoff score for cognitive impairment on the AM-PAC ACISF was <22. Administration time of the AM-PAC ACISF (1.0 min) was significantly less than that of the BCAT-SF (5.0 min) and the MoCA (13.3 min; p < .001). CONCLUSIONS AND RELEVANCE: Each screen demonstrated acceptable reliability and construct validity. The AM-PAC ACISF had the optimum mix of performance and feasibility for the fast-paced acute care setting. What This Article Adds: Early identification of cognitive impairment using the AM-PAC ACISF can allow for timely occupational therapy intervention in acute care settings.
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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 | 0.001 |
| 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 | 0.000 |
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".