Reliability and Validity of the Modified Mini Cog: A Measure for Screening Cognitive Functions in Literates and Non-literates
Bibliographic record
Abstract
BACKGROUND: The original Mini Cog was modified to make it applicable to non-literates as well. However the reliability and validity of the modified mini cog (MMC) has not been examined. Therefore, we aimed to investigate the intra- and inter-rater reliability, criterion validity, sensitivity and specificity of the MMC. METHODS: In this methodological and repeated measures design, elderly individuals (>60 years) with no neurological diagnosis or adults (>18 years) with neurological diagnosis were recruited from a tertiary hospital and the local community using purposive and snowball sampling. One of the raters administered the MMC twice (one week apart). Another rater administered the MMC and the Rowland Universal Dementia Assessment Scale (RUDAS) once during the first assessment session. RESULTS: The ICC for consistency of a rater across the tests and absolute agreement between two raters ranged from 0.97-0.99. The MMC scores of two raters were not significantly different. The MMC was able to differentiate between elderly participants with no neurological diagnosis and adult participants with neurological diagnosis. A significant correlation (Coefficients: 0.52-0.68) was found between the MMC and RUDAS. The sensitivity and specificity of the MMC were 86% and 70% respectively. The cutoff score of the MMC was found ? 3. CONCLUSIONS: We demonstrated an excellent intra- and inter-rater reliability, and adequate criterion validity, sensitivity and specificity of the MMC. The MMC was also able to discriminate various groups having varied level of cognitive dysfunction. However, further studies are warranted to get more insight into the value of this instrument.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".