Codex (Cognitive Disorders Examination) Decision Tree Modified for the Detection of Dementia and MCI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many cognitive screening instruments are available to assess patients with cognitive symptoms in whom a diagnosis of dementia or mild cognitive impairment is being considered. Most are quantitative scales with specified cut-off values. In contrast, the cognitive disorders examination or Codex is a two-step decision tree which incorporates components from the Mini-Mental State Examination (MMSE) (three word recall, spatial orientation) along with a simplified clock drawing test to produce categorical outcomes defining the probability of dementia diagnosis and, by implication, directing clinician response (reassurance, monitoring, further investigation, immediate treatment). Codex has been shown to have high sensitivity and specificity for dementia diagnosis but is less sensitive for the diagnosis of mild cognitive impairment (MCI). We examined minor modifications to the Codex decision tree to try to improve its sensitivity for the diagnosis of MCI, based on data extracted from studies of two other cognitive screening instruments, the Montreal Cognitive Assessment and Free-Cog, which are more stringent than MMSE in their tests of delayed recall. Neither modification proved of diagnostic value for mild cognitive impairment. Possible explanations for this failure are considered.
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.
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 | 0.000 |
| 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 it