Kristin Sainani is associate teaching professor of epidemiology and population health at Stanford University in California.
Kristin Sainani
Teaching professor
Stanford University
From this contributor
Journal Club: Meta-analysis oversells popular autism screen
The Modified Checklist for Autism in Toddlers (M-CHAT) accurately flags autistic toddlers, a new systematic review and meta-analysis suggests, contrary to past evidence that the tool’s validity varies depending on a child’s age and traits. Experts weigh in on the discrepancy.
Journal Club: Meta-analysis oversells popular autism screen
By
Spectrum
14 March 2023 | 5 min read
Flawed methods undermine study on undiagnosed autism and suicide
The researchers attempted to retroactively identify signs of autism in people who died by suicide, but their analysis is not convincing.
Flawed methods undermine study on undiagnosed autism and suicide
By
Kristin Sainani
28 February 2022 | 3 min read
Study links screen time to autism, but problems abound
The paper relied on parent-reported data and adjusted for few potentially confounding variables.
Study links screen time to autism, but problems abound
By
Kristin Sainani
1 February 2022 | 4 min read
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David Sussillo on persistence, luck and the bonds between life and work
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David Sussillo on persistence, luck and the bonds between life and work
In a Q&A about his new book, “Emergence,” Sussillo shares why he wrote it and how challenging circumstances shaped his journey into neuroscience.
By
Francisco J. Rivera Rosario, David Sussillo
17 March 2026 | 7 min watch
Leucovorin, long-read sequencing, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 16 March.
Leucovorin, long-read sequencing, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 16 March.
By
Jill Adams
17 March 2026 | 2 min read
Large-scale neuroimaging datasets often lack information specific to women’s health, constraining AI’s analysis potential
Addressing this gap will require collecting widespread data on pregnancy, menopause and other life events women experience—and could bring us closer to the “holy grail” of linking brain and behavior.
Large-scale neuroimaging datasets often lack information specific to women’s health, constraining AI’s analysis potential
Addressing this gap will require collecting widespread data on pregnancy, menopause and other life events women experience—and could bring us closer to the “holy grail” of linking brain and behavior.
By
Amy Kuceyeski
16 March 2026 | 0 min watch