P3‐040: Alzheimer's disease neuroimaging initiative: Baseline characteristics
Bibliographic record
Abstract
As new therapies for Alzheimer's disease (AD) are developed, there is a critical need to be able to assess their effectiveness, especially at the milder end of the disease spectrum. The ADNI is a multicenter study involving 59 sites in the U.S. and Canada designed to evaluate the utility of a variety of neuroimaging measures and biomarkers in normal, mild cognitive impairment (MCI) and mild AD subjects. The clinical assessment of the subjects included the following instruments: Mini-Mental State Exam (MMSE), Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog), Auditory Verbal Learning Test (AVLT), category fluency, digit span forward and backward, clock drawing, Boston Naming Test (BNT), Digit Symbol Substitution Test (DSST), Trail Making Test (TMT), American National Adult Reading Test(ANART). A total of 819 subjects were enrolled with the following demographic and cognitive characteristics at baseline: In addition, the three groups showed the following annual changes: normal, MCI, AD, respectively (mean, SE): ADAS-cog – 11: -0.5 (0.2), 1.1 (0.2), 4.2 (0.5); MMSE: -0.0 (0.1), -0.6 (0.1), -1.9 (0.3); CDR SoB: 0.1 (0.0), 0.6 (0.0), and 1.5 (0.1). These data indicate that the subjects recruited characterized typical populations of MCI and mild AD subjects for clinical trials. The baseline ADNI data serve as reference points to assess change over time and will be correlated with imaging and biomarker measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".