Validity of the Spanish version of the quick mild cognitive impairment screen (Q <i>mci</i> -S)
Bibliographic record
Abstract
Background Accurate, short, cognitive screening instruments are important for identifying mild cognitive impairment (MCI) and dementia including Alzheimer's disease. The Quick Mild Cognitive Impairment (Q mci ) screen, given its brevity, may be useful in this setting but it has not been validated in Spanish. Objective To compare the diagnostic accuracy of the Spanish version (Q mci -S) to the Montreal Cognitive Assessment (MoCA). Methods Patients aged ≥55 years were recruited from six Primary Care Teams in Barcelona, Spain, between 2018–2019. Dementia, MCI, and normal cognition (NC) were classified following neuropsychological testing, independent of the Q mci -S and MoCA scores. Diagnostic accuracy was determined by the area under receiver operating characteristic curves (AUC), adjusted for age and education. Results In total, 337 patients were included, mean 77.9 years, standard deviation (SD) ± 6.9. Most were female (57.3%). The intraclass correlation coefficient for the Q mci -S was excellent (0.98). The Q mci -S had good to excellent diagnostic accuracy for separating NC from MCI and dementia (AUC 0.91) and was statistically greater than the MoCA (AUC 0.86), p = 0.029. Accuracy was similar for differentiating NC from MCI and MCI from dementia. The Q mci -S had a shorter administer time, mean 8.6 (±3.2) versus 12.75 (SD ±5.5) minutes, respectively, p < 0.001. Conclusions The newly developed Q mci -S screen showed excellent criterion, construct and concurrent validity versus the widely used MoCA and had statistically similar, good to excellent, diagnostic accuracy for cognitive impairment. Its significantly shorter administration time suggests it may be the better screen in a Spanish speaking population in primary care, though further research is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".