Comparison between 4AT and mini-mental state examination tools for cognitive assessment of palliative care patients: A quality improvement project
Bibliographic record
Abstract
Background and objective: Cognitive impairment, including delirium, is common among patients with advanced cancer receiving palliative care, yet it often goes undetected. For the past 14 years, the Mini-Mental State Examination (MMSE), a globally recognized and validated tool, has been used by palliative care physicians in Qatar to assess cognitive function at the time of patient acceptance into the palliative care program. The aim of the study was to enhance the initial assessment of delirium and cognitive function in patients with advanced cancer by evaluating the feasibility and clinical utility of the Assessment Test for Delirium and Cognitive Impairment (4AT) screening tool alongside the MMSE. Methods: A quality improvement initiative was conducted using the Plan–Do–Study–Act (PDSA) methodology. Fifty-three patients with advanced cancer were assessed using both the MMSE and the 4AT tools (in Arabic and English) on the first day of acceptance into the palliative care program. Pre- and post-intervention surveys were used to capture palliative care physicians’ perceptions and experiences with both tools. Results: Physicians reported that the 4AT tool was simpler and faster to administer (100%), effective in assessing cognitive status (75%), provided useful clinical information (62.5%), and was well accepted by patients (100%). While the MMSE remains a well-established assessment tool, the 4AT was viewed as a more practical option for routine use in busy clinical settings. Conclusions: The 4AT tool demonstrates strong potential as a complementary or alternative approach to the MMSE for initial cognitive screening in palliative care. Its brevity, ease of use, and patient acceptability make it well-suited for routine clinical practice, particularly in settings requiring rapid cognitive assessment.
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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 | 0.063 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".