The A/T/N biomarker scheme and patterns of brain atrophy assessed in mild cognitive impairment
Bibliographic record
Abstract
The objective of this study was to evaluate the A/T/N biomarker scheme in relation with brain atrophy patterns in individuals with mild cognitive impairment (MCI). Of the 154 participants with MCI, 74 progressed to AD within 36-months, and 80 remained stable. In addition, 101 cognitively healthy participants and 102 participants with AD were included. The A/T/N classification was assessed with cerebrospinal fluid markers. Each individual was rated as either positive (abnormal) or negative (normal) on each biomarker. Brain atrophy was assessed with visual ratings from magnetic resonance imaging. None of the individuals with MCI progressed to AD if they had a negative "A" biomarker in conjunction with minimal atrophy. In contrary, several individuals with MCI progressed to AD if they had a positive "A" biomarker in conjunction with minimal atrophy. Numerous individuals with MCI showed inconsistency in the neurodegeneration domain ("N") regarding t-tau and atrophy. The assessment of the A/T/N classification scheme in addition with brain atrophy patterns in MCI, increases the knowledge of the clinical trajectories and the variability within the neurodegeneration domain. This emphasises that individuals with MCI display heterogeneous longitudinal patterns closely connected to their biomarker profiles, which could have important clinical implications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".