Age Corrections and Dementia Classification Accuracy
Bibliographic record
Abstract
In contrast to expectations, demographic corrections to reduce biases against those of advanced age or few years of education does not universally improve diagnostic classification accuracy. Age corrections may be particularly problematic because age is also a risk factor for a dementia diagnosis. We found that simulating increased risk for dementia based on demographic variables, such as age, reduced the overall classification accuracy for demographically corrected simulated scores relative to the raw, uncorrected test scores. In clinical data with a small magnitude of association between age and dementia diagnosis, we found equivalent overall classification accuracy for demographically corrected and raw test scores. Regardless of the overall classification accuracy results, cutoff comparisons (16th and 9th percentiles) in clinical and simulated data demonstrated that for the most part, the sensitivity of raw scores was higher than the sensitivity of demographically corrected scores, but the specificity of scores corrected with normative data was superior.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".