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Prediction of severe retinopathy of prematurity in 24–30 weeks gestation infants using birth characteristics

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References

  1. Pivodic A, Hård AL, Löfqvist C, Smith LEH, Wu C, Bründer M, et al. Individual risk prediction for sight –threatening retinopathy of prematurity using birth characteristics. JAMA Opthalmol. 2020;138:21–9.

    Article  Google Scholar 

  2. Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, et al. PROGRESS Group. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10:e1001381.

    Article  Google Scholar 

  3. Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 2019;170:W1–W33. https://www.equator-network.org/, accessed 02/18/2020.

    Article  Google Scholar 

  4. Raghuveer TS, Zackula RE. Strategies to prevent severe retinopathy: a 2020 update and meta-analysis. NeoReviews. 2020, in press.

  5. Darlow BA, Elder MJ, Horwood LJ, Donoghue DA, Henderson-Smart DJ. Australian and New Zealand Neonatal Network. Does observer bias contribute to variations in the rate of retinopathy of prematurity between centres? Clin Exp Ophthalmol. 2008;36(Jan-Feb):43–6.

    Article  Google Scholar 

  6. Wong RK, Ventura CV, Espiritu MJ, Yonekawa Y, Henchoz L, Chiang MF, et al. Training fellows for retinopathy of prematurity care: a web-based survey. J AAPOS. 2012;16:177–81.

    Article  Google Scholar 

  7. Hutchinson AK, Melia M, Yang MB, VanderVeen DK, Wilson LB, Lambert SR. Clinical models and algorithms for the prediction of Retinopathy of Prematurity. Ophthalmology. 2016;123:804–16.

    Article  Google Scholar 

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Acknowledgements

The Journal club is a collaboration between the American Academy of Pediatrics- Section of Neonatal Perinatal medicine and the International Society of Evidence-based neonatology (EBNEO.org).

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Correspondence to Talkad S. Raghuveer.

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Zackula, R.E., Raghuveer, T.S. Prediction of severe retinopathy of prematurity in 24–30 weeks gestation infants using birth characteristics. J Perinatol 41, 351–355 (2021). https://doi.org/10.1038/s41372-020-00876-9

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