Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis

Sarah J.E. Stock; Margaret Horne; Merel Bruijn; Helen White; Kathleen A. Boyd; Robert Heggie; Lisa Wotherspoon; Lorna S. Aucott; Rachel Morris; Jon Dorling; Lesley Jackson; Manju Chandiramani; Anna L. David; Asma Khalil; Andrew Shennan; Van Baaren Gert-Jan; Hodgetts Morton Victoria; Tina Lavender; Ewoud Schuit; Harper Clarke Susan; Ben W.J. Mol; Riley Richard; Jane E. Norman; John Norrie

doi:10.1371/journal.pmed.1003686

Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis

Sarah J.E. Stock, Margaret Horne, Merel Bruijn, Helen White, Kathleen A. Boyd, Robert Heggie, Lisa Wotherspoon, Lorna S. Aucott, Rachel Morris, Jon Dorling, Lesley Jackson, Manju Chandiramani, Anna L. David, Asma Khalil, Andrew Shennan, Van Baaren Gert-Jan, Hodgetts Morton Victoria, Tina Lavender, Ewoud Schuit, Harper Clarke SusanBen W.J. Mol, Riley Richard, Jane E. Norman, John Norrie

Research output: Contribution to journal › Review article › peer-review

22 Citations (Scopus)

Original language	English
Article number	e1003686
Journal	PLoS Medicine
Volume	18
Issue number	7
DOIs	https://doi.org/10.1371/journal.pmed.1003686
Publication status	Published - Jul 2021
Externally published	Yes

ASJC Scopus Subject Areas

General Medicine

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10.1371/journal.pmed.1003686

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Stock, S. J. E., Horne, M., Bruijn, M., White, H., Boyd, K. A., Heggie, R., Wotherspoon, L., Aucott, L. S., Morris, R., Dorling, J., Jackson, L., Chandiramani, M., David, A. L., Khalil, A., Shennan, A., Gert-Jan, V. B., Victoria, H. M., Lavender, T., Schuit, E., ... Norrie, J. (2021). Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis. PLoS Medicine, 18(7), Article e1003686. https://doi.org/10.1371/journal.pmed.1003686

Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis. / Stock, Sarah J.E.; Horne, Margaret; Bruijn, Merel et al.
In: PLoS Medicine, Vol. 18, No. 7, e1003686, 07.2021.

Research output: Contribution to journal › Review article › peer-review

Stock, SJE, Horne, M, Bruijn, M, White, H, Boyd, KA, Heggie, R, Wotherspoon, L, Aucott, LS, Morris, R, Dorling, J, Jackson, L, Chandiramani, M, David, AL, Khalil, A, Shennan, A, Gert-Jan, VB, Victoria, HM, Lavender, T, Schuit, E, Susan, HC, Mol, BWJ, Richard, R, Norman, JE & Norrie, J 2021, 'Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis', PLoS Medicine, vol. 18, no. 7, e1003686. https://doi.org/10.1371/journal.pmed.1003686

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author = "Stock, {Sarah J.E.} and Margaret Horne and Merel Bruijn and Helen White and Boyd, {Kathleen A.} and Robert Heggie and Lisa Wotherspoon and Aucott, {Lorna S.} and Rachel Morris and Jon Dorling and Lesley Jackson and Manju Chandiramani and David, {Anna L.} and Asma Khalil and Andrew Shennan and Gert-Jan, {Van Baaren} and Victoria, {Hodgetts Morton} and Tina Lavender and Ewoud Schuit and Susan, {Harper Clarke} and Mol, {Ben W.J.} and Riley Richard and Norman, {Jane E.} and John Norrie",

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AU - Heggie, Robert

AU - Wotherspoon, Lisa

AU - Aucott, Lorna S.

AU - Morris, Rachel

AU - Dorling, Jon

AU - Jackson, Lesley

AU - Chandiramani, Manju

AU - David, Anna L.

AU - Khalil, Asma

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AU - Mol, Ben W.J.

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Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis

ASJC Scopus Subject Areas

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