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Patient KC by Joanna Ferbrache, Cardiologist by Bryan Assata, Gynecologist by Star M Emergency Physician by Kazia Steele, Medical Oncologist by Eric Wolfgang Nelson Review (and KC's medical oncologist): Dr Pavlos Msaouel Production Assistants: Erin Goodenough, Nick Brown KC's YouTube Channel ► @katiekickscancer_kidney Chromophobe and Oncocytic Tumor Alliance: https://kidneycoa.org/ National Institute of Health, Katie Coleman: https://ccr.cancer.gov/news/article/all-about-the-drive-a-rare-kidney-cancer-meets-its-match-at-nih Katie received her second opinion and treatment at MD Anderson Cancer Center and her surgery was done at the National Cancer Institute, where 2 other confirmed metastatic oncocytoma cases are documented. Her case report to publish in a medical journal is currently pending. Big Data Paradox @HemeReview in-depth with Dr Msaouel ► https://youtu.be/fuFzv1AwBKQ Cancer Patient Speaks Candidly With Her Physician ► https://youtu.be/Nddft_nc3Yc @BigEmus Behind The Scenes ► https://youtu.be/vdyP0YtGfTA Katie's Story (playlist) ► https://www.youtube.com/playlist?list=PL26HeTCO57qcQzfyqkC6RG2ECAxQgAjCl Japanese Translation and Dub: Chihaya Hanai @rasha1m833 Japanese Voice KC: Kasumi Seki Spanish Translation and Dub: Cenzontle Voices, Inc CDMX Spanish Narration: Jerry Meza Spanish Voice KC: Jahel Morga Spanish Review: Dr Araceli Morga Vera Portuguese Translation and Dub: Cenzontle Voices, Inc CDMX Portuguese Translation: Zyanya Ponce Portuguese Narration: Santiago Álvarez Portuguese Voice KC: Gabriela Pellicia Portuguese Review: Lenin Sagrero Vietnamese translation and subtitles by Cao Nguyên Music by R Prince, performed by Chubbyemu Additional Music by @Lifeformed ► https://lifeformed.bandcamp.com Some Images by Getty Images Timestamps: 0:00 A Woman 💁‍♀️ Had A Headache Lasting 3 Days 0:05 KC is a 28 year old woman ☝️ presenting 🚑 to the emergency room 0:30 At the Urgent Care 0:58 Over the next few months 1:18 Abdominal pain 2:06 The anticipation of an imaging 👀 modality 3:03 Back in the 🚑 emergency room 4:12 A phenomenon of nature 5:06 An implication of heterogeneity 5:43 The mitochondria is the powerhouse of the cell, but something's 😳 wrong 6:41 Searching online cannot give you important contextual metadata 7:59 How do you know your plan is the one for you? 9:14 A powerful clinical reasoning tool 10:53 If you hear hooves think horses 🐴 not 🦓 zebras, but play telephone and people may hear ???? 12:32 Presenting☝️ to the emergency 🚑 room 13:04 This is called 'treatment resistance' These cases are patients who I, or my colleagues have seen. They are de-identified and many instances have been presented in more depth in an academic setting. These videos are not individual medical advice and are for general educational purposes only. I do not give medical advice over the internet. References: Metastatic renal oncocytoma. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1955059/ Renal oncocytoma, yet another tumour that does not fit in the dualistic benign/malignant paradigm? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1955085/ Oncocytoma and metastatic potential: myth or reality? https://link.springer.com/article/10.1007/BF00327061 Renal oncocytoma: clinicopathological study of 166 patients. J Urogenital Pathol 1991141–52. Classification of tumours. Pathology and genetics of tumours of the urinary system and male genital organs. Lyon: IARC Press, 2004 Oncocytoma. https://www.pathologyoutlines.com/topic/kidneytumoroncocytoma.html Chromophobe Kidney Cancer. https://www.pathologyoutlines.com/topic/kidneytumormalignantrccchromo.html Immunohistochemical analysis for cytokeratin 7, KIT, and PAX2: value in the differential diagnosis of chromophobe cell carcinoma. https://pubmed.ncbi.nlm.nih.gov/17210525/ The Big Data Paradox in Clinical Practice. https://www.tandfonline.com/doi/full/10.1080/07357907.2022.2084621 A Causal Framework for Making Individualized Treatment Decisions in Oncology. https://www.mdpi.com/2072-6694/14/16/3923 There is Individualized Treatment. Why Not Individualized Inference? https://arxiv.org/abs/1510.08539 Causal inference and the data-fusion problem. https://www.pnas.org/doi/10.1073/pnas.1510507113

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