PORTUGUESE VERSION OF THE QUICK MILD COGNITIVE IMPAIRMENT (QMCI-P) SCREEN—RESULTS FROM THE IBIS STUDY
Bibliographic record
Abstract
Background: As health professionals and researchers face more and more older adults and limited assessment times, there has been an increasing demand for shorter sensitive, reliable and valid cognitive screening instruments. Objective: To adapt the Qmci for Portuguese-language countries, to explore concurrent validity against most common short cognitive screening instruments - MMSE-P and MoCA-P and to assess correlations with other neurospychological dimensions. Methods: Demographic and clinical data (cognition, personality, depression and functionality) of older adults aged ≥65, attending ten day care centres (n=113) and residents in two long-term care institutions (n=53), were collected and assessed with short screening tools. Results: 148 individuals were screened with Qmci-P - median age of 77 (IQR +/-15); 64% female. 103 participants completed the assessment battery and those scoring ≥21 on GDS (indicating possible active depression) were excluded (n=11). The final sample (n=93) had a median age of 74 (IQR +/-15), significantly younger than all those initially consenting (p=0.03). Internal consistency of the Qmci-P using Cronbach’s Alpha was 0.823, better than with MoCA (0.79) and MMSE (0.54). The median Qmci-P score was 57/100 (IQR +/-26) with a median MoCA of 21/30 (IQR +/-8) and median SMMSE of 27/30 (IQR +/-5). Qmci-P screen scores were strongly, positively and significantly correlated with both MMSE (r=0.61, 95% CI 0.45–0.72, p<0.001) and MoCA (r=0.63, 95% CI 0.36–0.80, p<0.001). Conclusion: The Qmci-P is a valid short cognitive screen. Given the psychometric properties and brevity (3–5 minutes), it may be preferable for use than MMSE (7–8 minutes) and MOCA (10–12 minutes).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".