MRI and cerebrospinal fluid biomarkers for predicting progression to Alzheimer's disease in patients with mild cognitive impairment: a diagnostic accuracy study
Bibliographic record
Abstract
OBJECTIVES: To assess the incremental value of MRI and cerebrospinal fluid (CSF) analysis after a short memory test for predicting progression to Alzheimer's disease from a pragmatic clinical perspective. DESIGN: Diagnostic accuracy study in a multicentre prospective cohort study. SETTING: Alzheimer Disease Neuroimaging Initiative participants with complete data on neuropsychological assessment, MRI of the brain and CSF analysis. PARTICIPANTS: Patients with mild cognitive impairment (MCI; n=181) were included. Mean follow-up was 38.9 months (range 5.5-75.9). MAIN OUTCOME MEASURES: Diagnostic accuracy of individual instruments and incremental value of entorhinal cortex volume on MRI and p-τ/Aβ ration in CSF after administration of Rey's Auditory Verbal Learning Memory Test are calculated and expressed as the 'Net Reclassification Improvement' (NRI), which is the change in the percentage of individuals that are correctly diagnosed as Alzheimer or non-Alzheimer case. RESULTS: Tested in isolation, a short memory test, MRI and CSF all substantially contribute to the differentiation of those MCI patients who remain stable during follow-up from those who progress to develop Alzheimer's disease. The memory test, MRI and CSF improved the diagnostic classification by 21% (95% CI 15.1 to 26.9), 22.1% (95% CI 16.1 to 28.1) and 18.8% (95% CI 13.1 to 24.5), respectively. After administration of a short memory test, however, the NRI of MRI is +1.1% (95% CI 0.1 to 3.9) and of CSF is -2.2% (95% CI -5.6 to -0.6). CONCLUSIONS: After administration of a brief test of memory, MRI or CSF do not substantially affect diagnostic accuracy for predicting progression to Alzheimer's disease in patients with MCI. The NRI is an intuitive and easy to interpret measure for evaluation of potential added value of new diagnostic instruments in daily clinical practice.
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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 | 0.001 | 0.001 |
| 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 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".