P.006 Alzheimer’s disease CSF biomarker testing and its impacts on clinical management: findings from the IMPACT-AD BC study
Bibliographic record
Abstract
Background: Within the IMPACT-AD BC study, we sought to address the gap in knowledge around how the use of Alzheimer’s disease (AD) CSF biomarker testing impacts clinical management. Methods: IMPACT-AD BC (NCT05002699, impactAD.org) is an observational, longitudinal study examining the role of AD CSF biomarker testing (i.e., amyloid-beta and tau proteoforms) in medical and personal decision-making, and health economics. For medical decision-making, physicians completed surveys on patient management plans before and after receiving the biomarker findings. Overall change in management was assessed as a composite measure of changes in the use of: (i) AD symptomatic medications, (ii) other dementia-relevant medications, (iii) diagnostic procedures, and (iv) referrals or counselling. Results: Of the 142 participants, 66% were determined to have CSF biomarker profiles on the AD continuum. Overall change in management was observed in 89% of patients, with the greatest changes by category being: diagnostic procedures > referrals and counselling > AD symptomatic medications > other dementia-relevant medications. Conclusions: The use of AD CSF biomarker testing increases diagnostic confidence and aids in medical decision-making. Notably, the addition of biomarker testing leads to a reduction in the use of other diagnostic procedures, helps optimize pharmacotherapy and results in increased physician-patient/family member counselling.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".