Screening for cognitive impairment in an Australian aged care assessment team as part of comprehensive geriatric assessment
Bibliographic record
Abstract
Accurate detection of mild cognitive impairment (MCI) is important to stratify and address risk. Yet, few short cognitive screening instruments are validated for this. . In Australia, all clients referred to an Aged Care Assessment Team (ACAT) receive comprehensive geriatric assessment (CGA) including the Standardized Mini-Mental State Examination (SMMSE). We compared the accuracy of the quick mild cognitive impairment (Qmci) screen to the SMMSE in 283 participants: 195 with dementia, 47 with MCI, and 41 with subjective cognitive decline (SCD) in an Australian community-based ACAT. Both had similar accuracy in identifying dementia, AUC of 0.86 for the Qmci versus 0.93 for the SMMSE (p = 0.10), but the Qmci was more accurate than the SMMSE in differentiating MCI from SCD, AUC of 0.84 versus 0.71, respectively, p = 0.046. These suggest that the new, short (3-5 min) Qmci screenis appropriate for use in an ACAT or other units conducting CGA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".