Deans' stroke musings: Exploring the use of ChatGPT in predicting anterior circulation stroke functional outcomes after mechanical thrombectomy: a pilot study

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Thursday, March 14, 2024

Exploring the use of ChatGPT in predicting anterior circulation stroke functional outcomes after mechanical thrombectomy: a pilot study

What stupidity, survivors don't want to know about your predictions of failing to recover. They want recovery! DO THE GODDAMN RESEARCH THAT GETS THERE!

Exploring the use of ChatGPT in predicting anterior circulation stroke functional outcomes after mechanical thrombectomy: a pilot study

Tiago Pedro1,
José Maria Sousa1,
Luísa Fonseca2,3,
Manuel G. Gama2,3,
Goreti Moreira2,3,
Mariana Pintalhão2,3,
Paulo C. Chaves2,3,
Ana Aires3,4,
Gonçalo Alves1,5,
Luís Augusto1,5,
Luís Pinheiro Albuquerque1,5,
Pedro Castro4,6,
Maria Luís Silva1,5

Correspondence to Dr Tiago Pedro, Department of Neuroradiology, Centro Hospitalar Universitário de São João, Porto, 4200-319, Portugal; tiagoliveirapedro@hotmail.com

Abstract

Background Accurate prediction of functional outcomes is crucial in stroke management, but this remains challenging.(No it's not! Where the fuck did you get that stupid idea? Survivors don't give a fuck about your predictions of recovery failure! They want recovery! GET THERE!)

Objective To evaluate the performance of the generative language model ChatGPT in predicting the functional outcome of patients with acute ischemic stroke (AIS) 3 months after mechanical thrombectomy (MT) in order to assess whether ChatGPT can used to be accurately predict the modified Rankin Scale (mRS) score at 3 months post-thrombectomy.

Methods We conducted a retrospective analysis of clinical, neuroimaging, and procedure-related data from 163 patients with AIS undergoing MT. The agreement between ChatGPT’s exact and dichotomized predictions and actual mRS scores was assessed using Cohen’s κ. The added value of ChatGPT was measured by evaluating the agreement of predicted dichotomized outcomes using an existing validated score, the MT-DRAGON.

Results ChatGPT demonstrated fair (κ=0.354, 95% CI 0.260 to 0.448) and good (κ=0.727, 95% CI 0.620 to 0.833) agreement with the true exact and dichotomized mRS scores at 3 months, respectively, outperforming MT-DRAGON in overall and subgroup predictions. ChatGPT agreement was higher for patients with shorter last-time-seen-well-to-door delay, distal occlusions, and better modified Thrombolysis in Cerebral Infarction scores.

Conclusions ChatGPT adequately predicted short-term functional outcomes in post-thrombectomy patients with AIS and was better than the existing risk score. Integrating AI models into clinical practice holds promise for patient care, yet refining these models is crucial for enhanced accuracy in stroke management.