Correcting the 3MS for Bias Does Not Improve Accuracy When Screening for Cognitive Impairment or Dementia
Bibliographic record
Abstract
We investigated the effects of correcting for demographic biases on the sensitivity and specificity of the Modified Mini Mental Status Exam (3MS) using a sample of English-speaking older adults (N=8901) from the Canadian Studies of Health and Aging. The sensitivity and specificity of the original 3MS were compared to the 3MS regression-adjusted for the influence of demographic variables and then to 3MS percentiles based on published normative data with age and education corrected cutoff scores. According to receiver operating characteristic curve analyses, the regression-adjusted 3MS was no more accurate than the original 3MS when screening for dementia, and it was less accurate when screening for cognitive impairment. The use of 3MS percentiles based on normative data with age and education corrected cut-off points were less accurate than the original 3MS when screening for both cognitive impairment and when screening for dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".