Treatment of Dementia With Bosutinib: An Open-Label Study of a Tyrosine Kinase Inhibitor.
Bibliographic record
Abstract
ObjectiveThe pursuit of an effective therapeutic intervention for dementia has inspired interest in the class of medications known as tyrosine kinase inhibitors such as bosutinib.MethodsThirty-one patients with probable Alzheimer dementia or Parkinson spectrum disorder with dementia completed 12 months of bosutinib therapy and an additional 12 months of follow-up. The Clinical Dementia Rating scale (as estimated by the Quick Dementia Rating System [QDRS]) was the primary cognitive status outcome measure. Secondary outcome measures included the Repeatable Battery Assessment of Neuropsychological Status (RBANS) and the Montreal Cognitive Assessment. Cox regression methods were used to compare results with population-based estimates of cognitive decline.ResultsThe present article reports on cognitive outcomes obtained at 12 months for 31 participants and up to 24 months for a 16-participant subset. Safety and tolerability of bosutinib were confirmed among the study population (Mage = 73.7 years, SDage = 14 years). Bosutinib was associated with less worsening in Clinical Dementia Rating (CDR) scores (hazard ratio = -0.62, p < 0.001, 95% confidence interval [CI]: -1.02 to -0.30) and less decline in RBANS performance (hazard ratio = -3.42, p < 0.001, 95% CI: -3.59 to -3.72) during the year of treatment than population-based estimates of decline. In the 24-month follow-up, wherein 16 patients were observed after 1 year postintervention, 31.2% of participants exhibited worsened CDR levels compared with their 12-month performances.ConclusionsResults support an overall positive outcome after 1 year of bosutinib. Future studies should explore the relationship between tyrosine kinases and neurodegenerative pathology as well as related avenues of treatment.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".