Discrimination of the Cognitive Profiles of MCI and Depression using the KBNA
Bibliographic record
Abstract
OBJECTIVE: The current study sought to determine if the Kaplan-Baycrest Neurocognitive Assessment (KBNA) was capable of discriminating individuals with subjective memory complaints associated with depression from individuals with mild cognitive impairment (MCI). METHODS: Scores on 12 subtests of the KBNA were compared for 27 participants with MCI and 28 participants being treated for depression using Bonferroni correct between-group comparisons for each subtest. KBNA subtest scores were corrected for age and education. RESULTS: Significant between-group differences were obtained on six subtests with large effect sizes (Cohen's d) ranging from 1.19 - 1.58. The six subtests involved encoding and delayed episodic memory for verbal and visual information. Using logistic regression analysis, five subtests of the KBNA were able to correctly classify 96.4% of study participants. CONCLUSION: The results from this preliminary investigation indicate that the KBNA has the potential to serve as a brief and reliable assessment tool capable of distinguishing individuals with subjective memory complaints associated with depression from individuals with MCI in a clinical setting. Limitations of the current study and future research are discussed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".