Diagnostic Impact of Cerebrospinal Fluid Biomarkers in Atypical Dementias in Canada
Bibliographic record
Abstract
BACKGROUND: In Canada, standard dementia workup consists of clinical, neurological, and cognitive evaluation, as well as structural brain imaging. For atypical dementia presentations, additional FDG-PET brain imaging is recommended. Cerebrospinal fluid (CSF) biomarkers have recently been proposed as the gold standard for in vivo detection of Alzheimer's disease (AD) pathophysiology (NIA-AA research framework, 2018). As clinical implementation of CSF assessment is still limited in Canada, the present study assessed its impact on diagnostic accuracy in atypical neurodegenerative disorders in the clinical practice. METHODS: This retrospective clinical chart review included patients with cognitive complaints who underwent lumbar puncture (LP) in addition to the standard diagnostic workup. CSF analysis determined the presence of biological AD based on reduced amyloid-β42-to-total-tau index (ATI) and increased phosphorylated-tau (p-tau) levels. CSF-based diagnoses were compared to standard workup and FDG-PET-based diagnoses. RESULTS: A total of 28 patients with atypical dementia presentations were included in the present study after evaluation for cognitive complaints at a specialized dementia clinic between November 2017 and July 2019. CSF analysis changed or better specified the initial clinical diagnosis in 43.0% of cases (alternative diagnosis revealed in 25% and excluded in 18%). In patients with additional FDG-PET imaging (n = 23), FDG-PET and CSF-based diagnosis did not correspond in 35% of patients, even though FDG-PET appeared to increase diagnostic accuracy compared to the initial clinical diagnosis. CONCLUSION: CSF biomarkers improved diagnostic accuracy in atypical cognitively-impaired patients beyond standard workup and FDG-PET imaging. These results support CSF analysis implementation for atypical dementias in Canada, in addition to the standard diagnostic workup.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".