Specialized infrastructure in a tertiary hospital to administer disease modifying treatments in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Introduction Lecanemab (LEQEMBI®), a humanized monoclonal antibody targeting Amyloid‐beta (Aβ) protofibrils, received full FDA approval in July 2023 for treating early‐stage Alzheimer’s disease (AD). This abstract highlights Tel Aviv Medical Center’s (TLVMC) specialized infrastructure for early AD diagnosis and treatment and includes presenting baseline characteristics of initial patients opting for LEQEMBI®. Methods Outlining our clinics' operational experience in establishing the Center for advanced treatments for AD, treatment protocol, and a descriptive analysis of baseline assessment data including demographics, baseline Magnetic‐Resonance‐Imaging (MRI), Cerebrospinal‐fluid (CSF)/PET biomarkers, pre‐treatment cognitive evaluations (Mini‐Mental‐State‐Examination (MMSE)/Montreal‐Cognitive‐Assessment (MoCA)), and Apolipoprotein‐E (APOE) status. Results Rigorous screening and specialist approval were prerequisites for candidate selection, adhering with published appropriate use recommendations. All patients, diagnosed with Mild‐Cognitive‐Impairment (MCI)/mild dementia due to AD confirmed by biomarkers, underwent APOE testing. Lecanemab Administration commenced in Israel in November 2023, with 31 patients (F = 15, mean age = 72) initiating treatment by year‐end. 22 underwent neurological evaluation within a year of symptom onset. Mean MMSE = 24 (n = 27, SD = 3) mean MoCA = 23 (n = 4, SD = 2). CSF testing (27 patients), showed mean levels (pg/ml) of total‐Tau = 585 (SD = 251), phosphorylated‐Tau = 121 (SD = 38) and Aβ = 261 (SD = 172). APOE Ɛ4 polymorphism was present in 55%, with 18% showing 1‐2 micro‐hemorrhages on baseline MRI, compared to 8% of non‐carriers. Conclusion The evolving landscape of AD treatments necessitates complex screening and comprehensive clinical‐radiological follow‐up. Establishing specialized medical infrastructures is crucial to enable the wide and safe administration of new treatments, reflecting the ongoing paradigm shift in AD management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.009 |
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".