Tootfinder

tl;dr:
PBMs make kickback deals with pharmaceutical manufacturers to keep branded drugs profitable. https://mastodon.social/@blogdiva/115216099306903312

Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death
Sean McGrath, Takuya Kawahara, Joshua Petimar, Sheryl L. Rifas-Shiman, Iv\'an D\'iaz, Jason P. Block, Jessica G. Young
https://arxiv.org/abs/2509.13971

Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death
Researchers are often interested in estimating effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. For example, the Medications and Weight Gain in PCORnet (MedWeight) study aimed to estimate effects of adhering to flexible medication regimes on future weight change using electronic health records (EHR) data. This problem presents several methodological challenges that have not been jointly addressed in the prior…

For me... 12mm stone blocking the urtethra. Once they blasted it and function slowly (and painfully) got back to normal.
https://www.ctvnews.ca/health/article/what-does-it-mean-if-you-suddenly-cant-pee/

It’s amazing how many replies I’m getting that “vibe coding is fine, it doesn’t matter if software actually works”.
People literally die or get hurt all the time when websites or apps don’t work, because they get false information or they’re incorrectly flagged or they hit some edge case and stuff just doesn’t happen.
Power gets turned off. Medications don’t arrive. Accounts get frozen. Credit scores ruined. Personal information leaked.
If you don’t take making software seriously you should fuck right off and leave the field.

More reasonable distance today, better weather, meh AQI. Hoping to get in a few more runs in this week. Dropping some medications, so far it has helped a bit in some areas of #longfcovid recovery.
#running

Automated Hierarchical Graph Construction for Multi-source Electronic Health Records
Yinjie Wang, Doudou Zhou, Yue Liu, Junwei Lu, Tianxi Cai
https://arxiv.org/abs/2509.06576 ht…

Automated Hierarchical Graph Construction for Multi-source Electronic Health Records
Electronic Health Records (EHRs), comprising diverse clinical data such as diagnoses, medications, and laboratory results, hold great promise for translational research. EHR-derived data have advanced disease prevention, improved clinical trial recruitment, and generated real-world evidence. Synthesizing EHRs across institutions enables large-scale, generalizable studies that capture rare diseases and population diversity, but remains hindered by the heterogeneity of medical codes, institution-…

💉 Research identifies genetic predictor of weight loss with GLP-1RAs
#medicine

Research identifies genetic predictor of weight loss with GLP-1RAs
A new study published by Cleveland Clinic researchers in Diabetes, Obesity and Metabolism investigating the genetic underpinnings of weight loss response has identified a gene, neurobeachin (NBEA), as a predictor of how individuals respond to GLP-1RAs, the class of medications behind popular treatments like liraglutide and semaglutide.

More reasonable distance today, better weather, meh AQI. Hoping to get in a few more runs in this week. Dropping some medications, so far it has helped a bit in some areas of #longfcovid recovery.
#running

The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis
Shahriar Noroozizadeh, Pim Welle, Jeremy C. Weiss, George H. Chen
https://arxiv.org/abs/2506.18187

The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis
This study quantifies the association between non-adherence to antipsychotic medications and adverse outcomes in individuals with schizophrenia. We frame the problem using survival analysis, focusing on the time to the earliest of several adverse events (early death, involuntary hospitalization, jail booking). We extend standard causal inference methods (T-learner, S-learner, nearest neighbor matching) to utilize various survival models to estimate individual and average treatment effects, wher…

A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume
Yinghan Li, Yilin Yao, Junghua Lin, Nanxi Wang
https://arxiv.org/abs/2506.21927

A Deep Learning Algorithm Based on CNN-LSTM Framework for Predicting Cancer Drug Sales Volume
This study explores the application potential of a deep learning model based on the CNN-LSTM framework in forecasting the sales volume of cancer drugs, with a focus on modeling complex time series data. As advancements in medical technology and cancer treatment continue, the demand for oncology medications is steadily increasing. Accurate forecasting of cancer drug sales plays a critical role in optimizing production planning, supply chain management, and healthcare policy formulation. The data…

VO₂ max is back, and a better pace, thanks to medications I'm sure. Uppers and downers for #ADHD and #LongCovid 🫠
This is perfect #running weather though: sunny, cool, and a sane level…