Margot Hedlin

Nocturnist at Memorial Sloan Kettering. Co-host of "At the Bedside" on @CoreIMPodcast. Ask me for book recommendations

[Margot Hedlin @MargotHedlin]

"Relational autonomy sees people as interconnected and interdependent, shaped by their culture, communities, and relationships with loved ones". Grateful for the opportunity to write about this topic in NEJM! https://t.co/OtkGLeDYR7

[CORE IM @COREIMpodcast]

1/ 🚨NEW: Clinician Grief: At the Bedside Segment

What happens to our grief when the work doesn’t stop?

🎧: https://t.co/uNbj47328u
📝: https://t.co/0x9wxTdYNY

[CORE IM @COREIMpodcast]

1/ 🚨 NEW #AtTheBedside: Moral Distress Part 2: Strategies & Solutions

The FIRST and most important step in addressing moral distress: 

❤️‍🩹Naming it

We must identify what we’re actually feeling

🖥️: https://t.co/tgbPZA8ZjQ

[CORE IM @COREIMpodcast]

1/ 🚨 NEW #AtTheBedside: Moral Distress Part 1: Definitions, Causes & Consequences

Time to really name, talk about, and hopefully unpack some of the hidden weight we carry in the practice of medicine ⤵️

🖥️: https://t.co/n2OyYICBbx
Sponsor: @Pan_Financial

[CORE IM @COREIMpodcast]

8/ Thanks to all!

Authors: @taylorcoxmd @ShreyaTrivediMD
Discussants: Maria Rosasco, MD @MargotHedlin @AaronTroyMD
Graphic: Dr. Jesse Powell
Audio: Jerome C. Reyes
Peer Review: Jenny L. Schreiber, M.D., Adam Strauss MD, Mahathi Komaragiri MD

[Margot Hedlin @MargotHedlin]

@ChristleNwora Take the lessons that you feel are useful, and thank you for your thoughtful criticism of the ones that may not work for you!

[Margot Hedlin @MargotHedlin]

@ChristleNwora Some words/actions cut deeper than others, and given my relative privilege I'm not one to judge how people feel

[CORE IM @COREIMpodcast]

1/ 🚨New Episode: Managing Conflict🚨
At the Bedside Segment

Sharpen your de-escalation skills!
🎧: https://t.co/ZX3DvWaf6w
🗒️: https://t.co/GjL9nXgBSZ
Sponsor: @GlassHealthHQ https://t.co/LTnKQE61sU

[Margot Hedlin @MargotHedlin]

@jdg29_de @leatharious @drdagly More cars more subways fewer cars!

[Swimming Canada / Natation Canada @SwimmingCanada]

2 Erics, 1 race 🏁

Both Eric Hedlin and Eric Brown are tapered and ready to go for the 10k open water race as the first 🇨🇦 men to hit the water.

Results: https://t.co/v9jGreU7IA

[Margot Hedlin @MargotHedlin]

For the avid NYC cyclists among us, check out this project by Mat Kladney! @TransAlt @kickstarter https://t.co/XtpoMYLoUi

[Margot Hedlin @MargotHedlin]

@bbheinz @jtrebach @emcrit @RyanMarino @AlanaKinrich @emrap_tweets @EM_RESUS @mdcalc @dinawallin @marklieber @cdmukasa @holympus @DGlaucomflecken @rebelinslacks @pedsmd2b I use Gmail! I have a separate account for my medical notes; I find Gmail's search feature helpful, and also organize with folders for different systems. It's a fairly ersatz set of notes, often with a de-identified vignette of a patient that motivated me to read up on the topic

[Margot Hedlin @MargotHedlin]

That's my little brother!!

[AK @_akhaliq]

Unsupervised Semantic Correspondence Using Stable Diffusion

show that, without any training, one can leverage this semantic knowledge within diffusion models to find semantic correspondences -- locations in multiple images that have the same semantic meaning. Specifically, given an image, we optimize the prompt embeddings of these models for maximum attention on the regions of interest. These optimized embeddings capture semantic information about the location, which can then be transferred to another image. By doing so we obtain results on par with the strongly supervised state of the art on the PF-Willow dataset and significantly outperform (20.9% relative for the SPair-71k dataset) any existing weakly or unsupervised method on PF-Willow, CUB-200 and SPair-71k datasets

paper page: https://t.co/V4uPWUE2Fu

[AK @_akhaliq]

Unsupervised Semantic Correspondence Using Stable Diffusion

show that, without any training, one can leverage this semantic knowledge within diffusion models to find semantic correspondences -- locations in multiple images that have the same semantic meaning. Specifically, given an image, we optimize the prompt embeddings of these models for maximum attention on the regions of interest. These optimized embeddings capture semantic information about the location, which can then be transferred to another image. By doing so we obtain results on par with the strongly supervised state of the art on the PF-Willow dataset and significantly outperform (20.9% relative for the SPair-71k dataset) any existing weakly or unsupervised method on PF-Willow, CUB-200 and SPair-71k datasets

paper page: https://t.co/V4uPWUE2Fu

[Gabriel Bosslet @gbosslet]

This is a podcast I did with @COREIMpodcast on disputed treatment requests in ICUs (when patients want something physicians think is not needed/appropriate/effective)

I am impressed with the amount of work the @COREIMpodcast puts into these episodes

[Margot Hedlin @MargotHedlin]

@Dr_RShatsky We just addressed this in our most recent podcast episode on Hope (including hope for miracles) https://t.co/VHyWbzHDg3

[CORE IM @COREIMpodcast]

🚨 Hope: At The Bedside Episode 🚨

A powerful episode on practical ways to approach the different types of #hopes patients may have!

🎧 / Show notes: https://t.co/qcQNTrk5Ui
Sponsor: https://t.co/5zGm9Qo6fB

[CORE IM @COREIMpodcast]

1/ 🚨 New Episode 🚨

Upstanders: Standing Up Against Microaggression
#AttheBedside Segment

Hear powerful personal stories of ways people have been #upstanders and ways others have not 🙏

🎧: https://t.co/CstGdTE2wT
Show notes: https://t.co/QzLrWU8ESf
Sponsor: @Pan_Financial