Clinical reporting following the quantification of cerebrospinal fluid biomarkers in Alzheimer's disease: An international overview
Bibliographic record
Abstract
Abstract Background The quantification of cerebrospinal fluid (CSF) biomarkers (Amyloid beta peptides [Aß1‐40 and Aß1‐42], t‐tau and p‐tau(181)) is progressively implemented in specialized laboratories as an aid for the multidisciplinary diagnosis of Alzheimer’s disease (AD). There is however a diversity of practices between centers related to pre‐analytical and analytical conditions, the calculation of ratios between analytes, the applied cut‐off, or the use of interpretation scales. Finally, for the same biochemical profile, the interpretation and reporting of results may differ from one center to another, which may raise questions about the commutability of the tests. So far, no consensus has been reached between the different laboratories involved to define the most appropriate conclusions/comments based on the profile of the quantified biomarkers. This work is an essential step towards a consensual harmonization of clinical reporting after CSF analysis in the context of AD diagnosis, as advocated by the "Biofluid Based Biomarkers PIA" working group of the Alzheimer's Association. Method We obtained, by means of a questionnaire, a description of the pre‐analytical and analytical protocols and examples of reporting from 40 centers located in 15 countries, i.e. in the majority of countries that have implemented clinical CSF tests for the diagnosis of AD. We then adopted a consensus approach to propose harmonized comments corresponding to different AD CSF biomarker profiles observed in patients. Result Pre‐analytical procedures were very similar, among the centers. Regarding the analytical part, more than 88% of the laboratories use automatized immunoassays and more than 83% measure Aß1‐40 and compute the Aß1‐42/Aß1‐40 ratio, in addition to the three core biomarkers (Aß1‐42, t‐tau and p‐tau(181)). The cut‐off values of biomarkers used by the different laboratories are widely dispersed. Delay before sending back the results is lower than 1 week in more than 34% of the laboratories. Conclusion Our results highlight the state of the art in terms of clinical CSF analysis in the context of AD. Harmonization of clinical reporting between different centers could benefit AD care, prevention and treatment strategies, as a common terminology will allow a better assessment of the prevalence of AD and the contribution of biochemical biomarkers to its diagnosis.
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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 | 0.126 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".