Molecular pathology of neurodegenerative diseases: principles and practice
Bibliographic record
Abstract
Neurodegenerative diseases are characterised by selective dysfunction and progressive loss of synapses and neurons associated with pathologically altered proteins that deposit primarily in the human brain and spinal cord. Recent discoveries have identified a spectrum of distinct immunohistochemically and biochemically detectable proteins, which serve as a basis for protein-based disease classification. Diagnostic criteria have been updated and disease staging procedures have been proposed. These are based on novel concepts which recognise that (1) most of these proteins follow a sequential distribution pattern in the brain suggesting a seeding mechanism and cell-to-cell propagation; (2) some of the neurodegeneration-associated proteins can be detected in peripheral organs; and (3) concomitant presence of neurodegeneration-associated proteins is more the rule than the exception. These concepts, together with the fact that the clinical symptoms do not unequivocally reflect the molecular pathological background, place the neuropathological examination at the centre of requirements for an accurate diagnosis. The need for quality control in biomarker development, clinical and neuroimaging studies, and evaluation of therapy trials, as well as an increasing demand for the general public to better understand human brain disorders, underlines the importance for a renaissance of postmortem neuropathological studies at this time. This review summarises recent advances in neuropathological diagnosis and reports novel aspects of relevance for general pathological practice.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".