Comparison of clinical and neuropathological diagnoses of neurodegenerative diseases in two centres from the Brains for Dementia Research (BDR) cohort
Bibliographic record
Abstract
Early detection and accurate diagnosis of neurodegenerative disorders may provide better epidemiological data, closer monitoring of disease progression and enable more specialised intervention. We analysed the clinical records and pathology of brain donations from 180 patients from two Brains for Dementia Research cohorts to determine the agreement between in-life clinical diagnosis and post-mortem pathological results. Clinical diagnosis was extracted from medical records and cases assigned into broad clinical groups; control, Alzheimer's disease (AD), vascular dementia (CVD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD) and combined diseases. Pathology was assessed blindly, and cases categorised into; control, intermediate AD, severe AD, CVD, AD and CVD combined, DLB, AD and DLB combined and frontotemporal lobar degeneration (FTLD), according to the major contributing pathologies. In more than a third of cases clinical diagnosis was different from final neuropathological diagnosis. The majority of AD, DLB and control clinical groups matched the pathological diagnosis; however, thirty-five percent of clinical AD cases showed additional prominent CVD or DLB pathology which had not been diagnosed clinically and twenty-five percent of clinical control cases were found to have intermediate Tau pathology (modified Braak stage III-IV) or CVD. CVD and AD + CVD clinical groups showed an average of only thirty-two percent pathological correlation, the majority actually having no CVD, and fifty-three percent of pathologically identified FTLD cases had been incorrectly clinically diagnosed. Our results underlie the importance of neuropathological confirmation of clinical diagnosis. The relatively low accuracy of clinical diagnosis demonstrates the need for standardised and validated diagnostic assessment procedures.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".