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Diagnostic yield of pulmonary embolism testing in patients presenting to the emergency department with syncope.

Research and practice in thrombosis and haemostasis

Kelly C, Bledsoe JR, Woller SC, Stevens SM, Jacobs JR, Butler AM, Quinn J.
PMID: 32110757
Res Pract Thromb Haemost. 2019 Dec 26;4(2):263-268. doi: 10.1002/rth2.12294. eCollection 2020 Feb.

BACKGROUND: Syncope occurs in 1 in 4 people during their lifetime and accounts for 1% to 1.5% of emergency department (ED) visits. Most causes of syncope are benign, but syncope may be caused by life-threatening conditions including pulmonary embolism...

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Kelly C, Bledsoe JR, Woller SC, et al. Diagnostic yield of pulmonary embolism testing in patients presenting to the emergency department with syncope. Res Pract Thromb Haemost. 2019;4(2):263-268doi: 10.1002/rth2.12294.
Kelly, C., Bledsoe, J. R., Woller, S. C., Stevens, S. M., Jacobs, J. R., Butler, A. M., & Quinn, J. (2020). Diagnostic yield of pulmonary embolism testing in patients presenting to the emergency department with syncope. Research and practice in thrombosis and haemostasis, 4(2), 263-268. https://doi.org/10.1002/rth2.12294
Kelly, Christopher, et al. "Diagnostic yield of pulmonary embolism testing in patients presenting to the emergency department with syncope." Research and practice in thrombosis and haemostasis vol. 4,2 (2020): 263-268. doi: https://doi.org/10.1002/rth2.12294
Kelly C, Bledsoe JR, Woller SC, Stevens SM, Jacobs JR, Butler AM, Quinn J. Diagnostic yield of pulmonary embolism testing in patients presenting to the emergency department with syncope. Res Pract Thromb Haemost. 2019 Dec 26;4(2):263-268. doi: 10.1002/rth2.12294. eCollection 2020 Feb. PMID: 32110757; PMCID: PMC7040541.
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Electronic pulmonary embolism clinical decision support and effect on yield of computerized tomographic pulmonary angiography: ePE-A pragmatic prospective cohort study.

Journal of the American College of Emergency Physicians open

Bledsoe JR, Kelly C, Stevens SM, Woller SC, Haug P, Lloyd JF, Allen TL, Butler AM, Jacobs JR, Elliott CG.
PMID: 34263250
J Am Coll Emerg Physicians Open. 2021 Jul 03;2(4):e12488. doi: 10.1002/emp2.12488. eCollection 2021 Aug.

OBJECTIVE: Multiple professional societies recommend pre-test probability (PTP) assessment prior to imaging in the evaluation of patients with suspected pulmonary embolism (PE), however, PTP testing remains uncommon, with imaging occurring frequently and rates of confirmed PE remaining low. The...

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Bledsoe JR, Kelly C, Stevens SM, et al. Electronic pulmonary embolism clinical decision support and effect on yield of computerized tomographic pulmonary angiography: ePE-A pragmatic prospective cohort study. J Am Coll Emerg Physicians Open. 2021;2(4):e12488doi: 10.1002/emp2.12488.
Bledsoe, J. R., Kelly, C., Stevens, S. M., Woller, S. C., Haug, P., Lloyd, J. F., Allen, T. L., Butler, A. M., Jacobs, J. R., & Elliott, C. G. (2021). Electronic pulmonary embolism clinical decision support and effect on yield of computerized tomographic pulmonary angiography: ePE-A pragmatic prospective cohort study. Journal of the American College of Emergency Physicians open, 2(4), e12488. https://doi.org/10.1002/emp2.12488
Bledsoe, Joseph R, et al. "Electronic pulmonary embolism clinical decision support and effect on yield of computerized tomographic pulmonary angiography: ePE-A pragmatic prospective cohort study." Journal of the American College of Emergency Physicians open vol. 2,4 (2021): e12488. doi: https://doi.org/10.1002/emp2.12488
Bledsoe JR, Kelly C, Stevens SM, Woller SC, Haug P, Lloyd JF, Allen TL, Butler AM, Jacobs JR, Elliott CG. Electronic pulmonary embolism clinical decision support and effect on yield of computerized tomographic pulmonary angiography: ePE-A pragmatic prospective cohort study. J Am Coll Emerg Physicians Open. 2021 Jul 03;2(4):e12488. doi: 10.1002/emp2.12488. eCollection 2021 Aug. PMID: 34263250; PMCID: PMC8254596.
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Addendum: International evaluation of an AI system for breast cancer screening.

Nature

McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling-Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly CJ, King D, Ledsam JR, Melnick D, Mostofi H, Peng L, Reicher JJ, Romera-Paredes B, Sidebottom R, Suleyman M, Tse D, Young KC, De Fauw J, Shetty S.
PMID: 33057216
Nature. 2020 Oct;586(7829):E19. doi: 10.1038/s41586-020-2679-9.

No abstract available.

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McKinney SM, Sieniek M, Godbole V, et al. Addendum: International evaluation of an AI system for breast cancer screening. Nature. 2020;586(7829):E19doi: 10.1038/s41586-020-2679-9.
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., Melnick, D., Mostofi, H., Peng, L., Reicher, J. J., Romera-Paredes, B., Sidebottom, R., Suleyman, M., Tse, D., Young, K. C., De Fauw, J., & Shetty, S. (2020). Addendum: International evaluation of an AI system for breast cancer screening. Nature, 586(7829), E19. https://doi.org/10.1038/s41586-020-2679-9
McKinney, Scott Mayer, et al. "Addendum: International evaluation of an AI system for breast cancer screening." Nature vol. 586,7829 (2020): E19. doi: https://doi.org/10.1038/s41586-020-2679-9
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling-Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly CJ, King D, Ledsam JR, Melnick D, Mostofi H, Peng L, Reicher JJ, Romera-Paredes B, Sidebottom R, Suleyman M, Tse D, Young KC, De Fauw J, Shetty S. Addendum: International evaluation of an AI system for breast cancer screening. Nature. 2020 Oct;586(7829):E19. doi: 10.1038/s41586-020-2679-9. PMID: 33057216.
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A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.

The Lancet. Digital health

Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam JR, Schmid MK, Balaskas K, Topol EJ, Bachmann LM, Keane PA, Denniston AK.
PMID: 33323251
Lancet Digit Health. 2019 Oct;1(6):e271-e297. doi: 10.1016/S2589-7500(19)30123-2. Epub 2019 Sep 25.

BACKGROUND: Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging.METHODS: In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase,...

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Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271-e297doi: 10.1016/S2589-7500(19)30123-2.
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., Ledsam, J. R., Schmid, M. K., Balaskas, K., Topol, E. J., Bachmann, L. M., Keane, P. A., & Denniston, A. K. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet. Digital health, 1(6), e271-e297. https://doi.org/10.1016/S2589-7500(19)30123-2
Liu, Xiaoxuan, et al. "A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis." The Lancet. Digital health vol. 1,6 (2019): e271-e297. doi: https://doi.org/10.1016/S2589-7500(19)30123-2
Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam JR, Schmid MK, Balaskas K, Topol EJ, Bachmann LM, Keane PA, Denniston AK. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019 Oct;1(6):e271-e297. doi: 10.1016/S2589-7500(19)30123-2. Epub 2019 Sep 25. PMID: 33323251.
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Remarks on Errors of Diagnosis.

Provincial medical & surgical journal

Beddome JR.
PMID: 21379809
Prov Med Surg J (1840). 1841 Dec 04;3(62):188. doi: 10.1136/bmj.s1-3.10.188.

No abstract available.

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Beddome JR. Remarks on Errors of Diagnosis. Prov Med Surg J (1840). 1841;3(62):188doi: 10.1136/bmj.s1-3.10.188.
Beddome, J. R. (1841). Remarks on Errors of Diagnosis. Provincial medical & surgical journal, 3(62), 188. https://doi.org/10.1136/bmj.s1-3.10.188
Beddome, J R. "Remarks on Errors of Diagnosis." Provincial medical & surgical journal vol. 3,62 (1841): 188. doi: https://doi.org/10.1136/bmj.s1-3.10.188
Beddome JR. Remarks on Errors of Diagnosis. Prov Med Surg J (1840). 1841 Dec 04;3(62):188. doi: 10.1136/bmj.s1-3.10.188. PMID: 21379809; PMCID: PMC2488868.
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Rapid and Deep Remission Induced by Blinatumomab for CD19-Positive Chronic Myeloid Leukemia in Lymphoid Blast Phase.

JCO precision oncology

Patel SA, Bledsoe JR, Higgins AW, Hutchinson L, Gerber JM.
PMID: 34409243
JCO Precis Oncol. 2021 Jul 09;5. doi: 10.1200/PO.21.00039. eCollection 2021 Jul.

No abstract available.

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Patel SA, Bledsoe JR, Higgins AW, et al. Rapid and Deep Remission Induced by Blinatumomab for CD19-Positive Chronic Myeloid Leukemia in Lymphoid Blast Phase. JCO Precis Oncol. 2021;5doi: 10.1200/PO.21.00039.
Patel, S. A., Bledsoe, J. R., Higgins, A. W., Hutchinson, L., & Gerber, J. M. (2021). Rapid and Deep Remission Induced by Blinatumomab for CD19-Positive Chronic Myeloid Leukemia in Lymphoid Blast Phase. JCO precision oncology, 5. https://doi.org/10.1200/PO.21.00039
Patel, Shyam A, et al. "Rapid and Deep Remission Induced by Blinatumomab for CD19-Positive Chronic Myeloid Leukemia in Lymphoid Blast Phase." JCO precision oncology vol. 5 (2021). doi: https://doi.org/10.1200/PO.21.00039
Patel SA, Bledsoe JR, Higgins AW, Hutchinson L, Gerber JM. Rapid and Deep Remission Induced by Blinatumomab for CD19-Positive Chronic Myeloid Leukemia in Lymphoid Blast Phase. JCO Precis Oncol. 2021 Jul 09;5. doi: 10.1200/PO.21.00039. eCollection 2021 Jul. PMID: 34409243; PMCID: PMC8367045.
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Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Nature protocols

Tomašev N, Harris N, Baur S, Mottram A, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Magliulo V, Meyer C, Ravuri S, Protsyuk I, Connell A, Hughes CO, Karthikesalingam A, Cornebise J, Montgomery H, Rees G, Laing C, Baker CR, Osborne TF, Reeves R, Hassabis D, King D, Suleyman M, Back T, Nielson C, Seneviratne MG, Ledsam JR, Mohamed S.
PMID: 33953393
Nat Protoc. 2021 Jun;16(6):2765-2787. doi: 10.1038/s41596-021-00513-5. Epub 2021 May 05.

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol...

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Tomašev N, Harris N, Baur S, et al. Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records. Nat Protoc. 2021;16(6):2765-2787doi: 10.1038/s41596-021-00513-5.
Tomašev, N., Harris, N., Baur, S., Mottram, A., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Magliulo, V., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Osborne, T. F., Reeves, R., Hassabis, D., King, D., Suleyman, M., Back, T., Nielson, C., Seneviratne, M. G., Ledsam, J. R., & Mohamed, S. (2021). Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records. Nature protocols, 16(6), 2765-2787. https://doi.org/10.1038/s41596-021-00513-5
Tomašev, Nenad, et al. "Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records." Nature protocols vol. 16,6 (2021): 2765-2787. doi: https://doi.org/10.1038/s41596-021-00513-5
Tomašev N, Harris N, Baur S, Mottram A, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Magliulo V, Meyer C, Ravuri S, Protsyuk I, Connell A, Hughes CO, Karthikesalingam A, Cornebise J, Montgomery H, Rees G, Laing C, Baker CR, Osborne TF, Reeves R, Hassabis D, King D, Suleyman M, Back T, Nielson C, Seneviratne MG, Ledsam JR, Mohamed S. Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records. Nat Protoc. 2021 Jun;16(6):2765-2787. doi: 10.1038/s41596-021-00513-5. Epub 2021 May 05. PMID: 33953393.
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Post-discharge thrombosis and bleeding in medical patients: A novel risk score derived from ubiquitous biomarkers.

Research and practice in thrombosis and haemostasis

Woller SC, Stevens SM, Fazili M, Lloyd JF, Wilson EL, Snow GL, Bledsoe JR, Horne BD.
PMID: 34263106
Res Pract Thromb Haemost. 2021 Jul 07;5(5):e12560. doi: 10.1002/rth2.12560. eCollection 2021 Jul.

BACKGROUND: Some hospitalized medical patients experience venous thromboembolism (VTE) following discharge. Prophylaxis extended beyond hospital discharge (extended duration thromboprophylaxis [EDT]) may reduce this risk. However, EDT is costly and can cause bleeding, so selecting appropriate patients is essential. We...

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Woller SC, Stevens SM, Fazili M, et al. Post-discharge thrombosis and bleeding in medical patients: A novel risk score derived from ubiquitous biomarkers. Res Pract Thromb Haemost. 2021;5(5):e12560doi: 10.1002/rth2.12560.
Woller, S. C., Stevens, S. M., Fazili, M., Lloyd, J. F., Wilson, E. L., Snow, G. L., Bledsoe, J. R., & Horne, B. D. (2021). Post-discharge thrombosis and bleeding in medical patients: A novel risk score derived from ubiquitous biomarkers. Research and practice in thrombosis and haemostasis, 5(5), e12560. https://doi.org/10.1002/rth2.12560
Woller, Scott C, et al. "Post-discharge thrombosis and bleeding in medical patients: A novel risk score derived from ubiquitous biomarkers." Research and practice in thrombosis and haemostasis vol. 5,5 (2021): e12560. doi: https://doi.org/10.1002/rth2.12560
Woller SC, Stevens SM, Fazili M, Lloyd JF, Wilson EL, Snow GL, Bledsoe JR, Horne BD. Post-discharge thrombosis and bleeding in medical patients: A novel risk score derived from ubiquitous biomarkers. Res Pract Thromb Haemost. 2021 Jul 07;5(5):e12560. doi: 10.1002/rth2.12560. eCollection 2021 Jul. PMID: 34263106; PMCID: PMC8265782.
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Clonal IgH gene rearrangements identify Richter's transformation to diffuse large B cell lymphoma.

Annals of hematology

Dalela D, Bledsoe JR, Patel SA.
PMID: 33159568
Ann Hematol. 2021 Dec;100(12):3075-3077. doi: 10.1007/s00277-020-04334-6. Epub 2020 Nov 07.

No abstract available.

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Dalela D, Bledsoe JR, Patel SA. Clonal IgH gene rearrangements identify Richter's transformation to diffuse large B cell lymphoma. Ann Hematol. 2020;100(12):3075-3077doi: 10.1007/s00277-020-04334-6.
Dalela, D., Bledsoe, J. R., & Patel, S. A. (2021). Clonal IgH gene rearrangements identify Richter's transformation to diffuse large B cell lymphoma. Annals of hematology, 100(12), 3075-3077. https://doi.org/10.1007/s00277-020-04334-6
Dalela, Disha, et al. "Clonal IgH gene rearrangements identify Richter's transformation to diffuse large B cell lymphoma." Annals of hematology vol. 100,12 (2021): 3075-3077. doi: https://doi.org/10.1007/s00277-020-04334-6
Dalela D, Bledsoe JR, Patel SA. Clonal IgH gene rearrangements identify Richter's transformation to diffuse large B cell lymphoma. Ann Hematol. 2021 Dec;100(12):3075-3077. doi: 10.1007/s00277-020-04334-6. Epub 2020 Nov 07. PMID: 33159568.
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A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.

The Lancet. Digital health

Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam JR, Schmid MK, Balaskas K, Topol EJ, Bachmann LM, Keane PA, Denniston AK.
PMID: 33323251
Lancet Digit Health. 2019 Oct;1(6):e271-e297. doi: 10.1016/S2589-7500(19)30123-2. Epub 2019 Sep 25.

BACKGROUND: Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging.METHODS: In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase,...

Cite
Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271-e297doi: 10.1016/S2589-7500(19)30123-2.
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., Ledsam, J. R., Schmid, M. K., Balaskas, K., Topol, E. J., Bachmann, L. M., Keane, P. A., & Denniston, A. K. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet. Digital health, 1(6), e271-e297. https://doi.org/10.1016/S2589-7500(19)30123-2
Liu, Xiaoxuan, et al. "A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis." The Lancet. Digital health vol. 1,6 (2019): e271-e297. doi: https://doi.org/10.1016/S2589-7500(19)30123-2
Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam JR, Schmid MK, Balaskas K, Topol EJ, Bachmann LM, Keane PA, Denniston AK. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019 Oct;1(6):e271-e297. doi: 10.1016/S2589-7500(19)30123-2. Epub 2019 Sep 25. PMID: 33323251.
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Polyclonal PAX8 expression in carcinomas of the biliary tract - Frequent non-specific staining represents a potential diagnostic pitfall.

Annals of diagnostic pathology

Zong Y, Xiong Y, Dresser K, Yang M, Bledsoe JR.
PMID: 34102541
Ann Diagn Pathol. 2021 Aug;53:151762. doi: 10.1016/j.anndiagpath.2021.151762. Epub 2021 May 26.

Paired box protein 8 (PAX8) is a transcription factor that is considered a relatively specific marker of carcinomas of the thyroid, kidney, and Müllerian/Wolffian duct derivatives. Unexpected PAX8 immunoreactivity has occasionally been reported in other tumors. The frequency of...

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Zong Y, Xiong Y, Dresser K, et al. Polyclonal PAX8 expression in carcinomas of the biliary tract - Frequent non-specific staining represents a potential diagnostic pitfall. Ann Diagn Pathol. 2021;53:151762doi: 10.1016/j.anndiagpath.2021.151762.
Zong, Y., Xiong, Y., Dresser, K., Yang, M., & Bledsoe, J. R. (2021). Polyclonal PAX8 expression in carcinomas of the biliary tract - Frequent non-specific staining represents a potential diagnostic pitfall. Annals of diagnostic pathology, 53151762. https://doi.org/10.1016/j.anndiagpath.2021.151762
Zong, Yang, et al. "Polyclonal PAX8 expression in carcinomas of the biliary tract - Frequent non-specific staining represents a potential diagnostic pitfall." Annals of diagnostic pathology vol. 53 (2021): 151762. doi: https://doi.org/10.1016/j.anndiagpath.2021.151762
Zong Y, Xiong Y, Dresser K, Yang M, Bledsoe JR. Polyclonal PAX8 expression in carcinomas of the biliary tract - Frequent non-specific staining represents a potential diagnostic pitfall. Ann Diagn Pathol. 2021 Aug;53:151762. doi: 10.1016/j.anndiagpath.2021.151762. Epub 2021 May 26. PMID: 34102541.
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Reply to "Limitations of multivariate survival analysis".

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

Zhu X, Zou J, Mochel MC, Bledsoe JR.
PMID: 34963693
Mod Pathol. 2021 Dec 28; doi: 10.1038/s41379-021-00988-3. Epub 2021 Dec 28.

No abstract available.

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Zhu X, Zou J, Mochel MC, et al. Reply to "Limitations of multivariate survival analysis". Mod Pathol. 2021;doi: 10.1038/s41379-021-00988-3.
Zhu, X., Zou, J., Mochel, M. C., & Bledsoe, J. R. (2021). Reply to "Limitations of multivariate survival analysis". Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, . https://doi.org/10.1038/s41379-021-00988-3
Zhu, Xiaoqin, et al. "Reply to "Limitations of multivariate survival analysis"." Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc vol. (2021). doi: https://doi.org/10.1038/s41379-021-00988-3
Zhu X, Zou J, Mochel MC, Bledsoe JR. Reply to "Limitations of multivariate survival analysis". Mod Pathol. 2021 Dec 28; doi: 10.1038/s41379-021-00988-3. Epub 2021 Dec 28. PMID: 34963693.
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