F4‐02‐03: LITHIUM TREATMENT OF AGITATION IN ALZHEIMER'S DISEASE (LIT‐AD): CLINICAL RATIONALE AND STUDY DESIGN
Bibliographic record
Abstract
In patients with Alzheimer's disease (AD), symptoms of agitation, aggression and psychosis are common, distressing to patients and caregivers, difficult to treat effectively, often lead to institutionalization, and associated with increased mortality and health care costs. Lithium is an established treatment for bipolar and other psychotic disorders. The Lit-AD study is the first randomized, double-blind, placebo-controlled trial to assess the efficacy of lithium treatment for symptoms of agitation with or without psychosis in patients with a clinical diagnosis of possible or probable AD. Eighty outpatients are recruited at four sites: New York State Psychiatric Institute/Columbia University Medical Center, New York (coordinating center and lead site); University of Miami Miller School of Medicine; McLean Hospital, Harvard Medical School, Belmont, MA; University of Texas Southwestern Medical Center, Dallas, TX. Patients are assigned randomly to low dose lithium (150-600 mg daily) or placebo in this 12-week clinical trial with a targeted blood level of 0.2-0.6 mmol/l. A physician independent of the study conveys real and sham lithium levels to the study physician, who then makes dose adjustments while the blind is maintained. The primary hypothesis is that the agitation/aggression domain score on the Neuropsychiatric Inventory (NPI) will decrease more on lithium than placebo. The secondary hypothesis is that there will be a greater number of responders on lithium than placebo. Change in NPI psychosis scores on lithium compared to placebo will be explored. Treatment Emergent Symptom Scale (TESS) scores and emergent adverse events will be used to assess tolerability. Exploratory outcomes include change in serum brain-derived neurotrophic factor (BDNF) levels and two genetic markers as potential predictors of response to lithium: a specific SNP on intron 1 of the ACCN1 gene, and variation at the 7q11.2 gene locus. Trial Design; blinded study is in progress. This study will provide initial data on three potential roles for low-dose lithium in clinical practice: (1) first-line treatment; (2) adjunct treatment in partial responders; (3) second-line agent after non-response or intolerability to other medications. The results of this Phase II trial will determine the potential of a large-scale Phase III clinical trial of lithium in these patients.
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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 | 0.022 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".