The Predictive Value of Revised Petersen’s Criteria in Detection of Mild Cognitive Impairment in A Sample of Community-Dwelling Egyptians
Bibliographic record
Abstract
BackgroundMild cognitive impairment is a condition that transitions to dementia in most of the cases. Early detection and management with the correction of modifiable risk factors is the only way to reduce the burden of dementia. Hence, using proper screening tools for mild cognitive impairment is warranted. This study aimed to test the validity of revised Petersen’s criteria in the screening for mild cognitive impairment among a sample of community-dwelling Egyptian elderly. MethodsA cross-sectional study including 106 elderly patients was done. The Montreal Cognitive Assessment (MoCA) test was used as the gold standard test to diagnose mild cognitive impairment (MCI). Revised Petersen`s criteria were applied to all participants. Patients with dementia, depression, severe hearing or visual impairment, and physical or neurological disease that hinder their ability to perform the tests were excluded from the study.ResultsThe prevalence of MCI in the study sample was 70.8% using MoCA. Compared to MoCA, Revised Petersen criteria had high specificity (90.3%) and positive predictive value (92.7%), but low sensitivity (50.7%).ConclusionThe Arabic version of MoCA used cut-off points that need re-evaluation in the Egyptian population as it is unlikely that the percentage of MCI among community-dwelling elderly be that high (70.8%). With such a low sensitivity revised Petersen’s criteria cannot be used for screening of MCI. It can be applied for confirmation of MCI cases (Specificity 90%).
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".