Validation of the Identification and Intervention for Dementia in Elderly Africans (IDEA) cognitive screen in Nigeria and Tanzania
Bibliographic record
Abstract
BACKGROUND: We have previously described the development of the Identification and Intervention for Dementia in Elderly Africans (IDEA) cognitive screen for use in populations with low levels of formal education. The IDEA cognitive screen was developed and field-tested in an elderly, community-based population in rural Tanzania with a relatively high prevalence of cognitive impairment. The aim of this study was to validate the IDEA cognitive screen as an assessment of major cognitive impairment in hospital settings in Nigeria and Tanzania. METHODS: In Nigeria, 121 consecutive elderly medical clinic outpatients reviewed at the University College Hospital, Ibadan were screened using the IDEA cognitive screen. In Tanzania, 97 consecutive inpatients admitted to Mawenzi Regional Hospital (MRH), Moshi, and 108 consecutive medical clinic outpatients attending the geriatric medicine clinic at MRH were screened. Inter-rater reliability was assessed in Tanzanian outpatients attending St Joseph's Hospital in Moshi using three raters. A diagnosis of dementia or delirium (DSM-IV criteria) was classified as major cognitive impairment and was provided independently by a physician blinded to the results of the screening assessment. RESULTS: The area under the receiver operating characteristic (AUROC) curve in Nigerian outpatients, Tanzanian outpatients and Tanzanian inpatients was 0.990, 0.919 and 0.917 respectively. Inter-rater reliability was good (intra-class correlation coefficient 0.742 to 0.791). In regression models, the cognitive screen did not appear to be educationally biased. CONCLUSIONS: The IDEA cognitive screen performed well in these populations and should prove useful in screening for dementia and delirium in other areas of sub-Saharan Africa.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".