Dynamic Individual Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer’s Disease using Joint Modeling
Bibliographic record
Abstract
ABSTRACT Background We propose a joint model predicting the risk of conversion from MCI to AD that considers the association between biomarker evolution and disease progression. Methods We selected 814 MCI subjects (285 progressives, 529 stables) who had at least four follow-up MRI visits from the ADNI dataset. The values of Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-Cog) were used as a surrogate of time. A mixed linear model was fitted for bilateral hippocampal volumes (HC) versus ADAS-Cog, education, age and sex and a Cox model for risk progression. The association between HC evolution and risk conversion was estimated by fitting a joint model. Results Our results show (1) significant association ( p < .0001, C.I.= [0.0864; 0.1217]) between bilateral HC and risk of conversion; (2) on average, the risk of progression increased as HC decreased; and (3) the individual prediction of the risk is dynamic, i.e., updated at each follow-up. The AUC of our model for the whole group increased to reach 0.789 at the last follow-up. Conclusions Applicable to AD and generalizable to other biomarkers and covariates, this joint methodology has a direct application in the clinical estimation of individual risk.
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.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".