Validation of the Turkish Version of the Quick Mild Cognitive Impairment Screen
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to validate the Turkish version of the Quick Mild Cognitive Impairment (Q mci-TR) screen. METHODS: In total, 100 patients aged ≥65 years referred to a geriatric outpatient clinic with memory loss were included. The Q mci was compared to the Turkish versions of the standardized Mini-Mental State Examination and the Montreal Cognitive Assessment (MoCA). RESULTS: The Q mci-TR had higher accuracy than the MoCA in discriminating subjective memory complaints (SMCs) from cognitive impairment (mild cognitive impairment [MCI] or dementia), of borderline significance after adjusting for age and education ( P = .06). The Q mci-TR also had higher accuracy than the MoCA in differentiating MCI from SMC, which became nonsignificant after adjustment ( P = .15). A similar pattern was shown for distinguishing MCI from dementia. Test reliability for the Q mci-TR was strong. CONCLUSION: The Q mci-TR is a reliable and useful screening tool for discriminating MCI from SMC and dementia in a Turkish population.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".