P.005 Real-world evidence of Lecanemab use in the United Statese
Bibliographic record
Abstract
Background: Lecanemab is the first anti-amyloid monoclonal antibody to receive full approval in the US for early Alzheimer’s Disease (AD). Methods: Using open administrative claims from the PurpleLab (6Jan2023–1Aug2024) database, patients receiving ≥1 lecanemab and with continuous clinical activity ≥6 months prior to the first lecanemab infusion were included. The follow-up period ran from the first lecanemab administration to the latest clinical activity. Treatment gap was calculated as the number of days without lecanemab supply between consecutive infusions. Results: A total of 2,840 patients were included. Mean observation period was 130.7 days. Mean age was 75.4 (SD 6.2) years, and 54.8% were female. Most prescribers were neurologists (82.0%). Within 30 days before lecanemab initiation, 77.0% of patients had AD diagnosis, and 32.1% had mild cognitive impairment diagnosis. During lecanemab treatment, 27.2% of patients received cholinesterase inhibitors and 15.5% memantine. Among patients with ≥2 lecanemab infusions, average number of administrations per month was 1.9 (SD 0.4), 17.2 (SD 7.9) days apart; 9.9% had a treatment gap of ≥90 days, including those who discontinued or continuing beyond the gap, and 2.5% of patients experienced a treatment interruption with ≥90 days gap. Conclusions: Real-world use of lecanemab appears to follow FDA-approved prescribing information with high adherence.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".