Classification accuracy of the English version of the Canadian Indigenous Cognitive Assessment (CICA) in a majority culture memory clinic sample
Bibliographic record
Abstract
Abstract Background Most cognitive screening tools are culturally inappropriate for Indigenous peoples. The Kimberly Indigenous Cognitive Assessment (KICA) screening tool was collaboratively developed in Australia with, and for, Indigenous peoples. Using a similar community‐based method, the KICA was adapted to Canadian Indigenous peoples’ culture and translated into other languages, beginning in Ontario on Manitoulin Island, and was re‐named the Canadian Indigenous Cognitive Assessment (CICA). The classification accuracy of the CICA‐Ontario version, English translation, was explored in the current project in a memory clinic setting where the majority of patients were of Eurocentric origin. Method Of 79 consecutive cases presenting to an interdisciplinary diagnostic memory clinic, 58 completed the CICA. Inclusion criteria included consent for participation in research and planned administration of the typical 2 hour long neuropsychological battery. CICA data were not used for any clinical process; consequently, diagnosis was independent. Diagnosis was made by consensus of the neurologist and neuropsychologist after patient and collateral informant interviews and nursing, neurological, medical (comprehensive blood work, neuroimaging), neuropsychological, and physical therapy evaluations. Result Patients’ ages ranged between 38 and 90 (M= 65.3, SD= 12.3) and 52 percent were male. Diagnoses included no objective cognitive impairment despite cognitive concerns termed subjective cognitive impairment (SCI; n= 23; CICA M = 37.09; CICA SD = 1.08), mild cognitive impairment (MCI; n= 14; CICA M = 34.93; CICA SD = 2.46), dementia due to Alzheimer disease (AD; n= 9; CICA M = 31.44; CICA SD = 3.00), and non‐AD dementias (n= 12; CICA M = 32.75; CICA SD = 5.07). Classification accuracy as measured by the area under the receiver operating characteristic (ROC) curve (AUC) for groups with diagnoses of dementia (n= 21) vs no dementia (SCI or MCI; n= 37) was .78 (AUC confidence interval (CI) .66‐.92). Accuracy was high for cognitive impairment (dementia and MCI; n= 35) versus SCI (n= 23) (AUC=.84 CI .74‐.94) and lower for MCI (n= 14) vs dementia (n= 14)(AUC=.67; CI .49‐.85). Conclusion Although additional validity data from Indigenous peoples is needed, these data support the measurement properties of the CICA‐Ontario English‐version in a majority culture sample.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".