Rockwood, Green, and Wilkins' Fractures, 10e Package

129

CHAPTER 5 • Classification of Fracture

Annotated References

Reference

Annotation

Orthopaedic Trauma Association: Open Fracture Study Group. A new classification scheme for open fractures. J Orthop Trauma. 2010;24(8): 457–464.

An expert panel reviewed 34 factors felt to be important in the management of open fractures. Five categories were deemed essential in determination of open fracture severity: skin injury, muscle injury, arterial injury, contamination and bone loss. The study was designed to determine whether the Sanders CT scan classification could be prognostic for long-term (10–20 years) radiographic and functional outcomes. Based on the results, the Sanders classification remains prognostic after a minimum 10 years. Of note type III fractures were four times more likely to need fusion than type II fractures. A standardized video review of periarticular soft tissue injuries by 15 reviewers was performed. The Tscherne classification produced an adequate level of agreement (kappa 0.65) and could be used to standardize and guide treatment. 22. Gustilo RB, Mendoza RM, Williams DN. Problems in the management of type III (severe) open fractures: a new classification of type III open fractures. J Trauma . 1984;24(8):742–746. 23. Hertel R, Hempfing A, Stiehler M, Leunig M. Predictors of humeral head ischemia after intracapsular fracture of the proximal humerus. J Shoulder Elbow Surg . 2004; 13(4):427–433. 24. Horn BD, Rettig ME. Interobserver reliability in the Gustilo and Anderson classifica tion of open fractures. J Orthop Trauma . 1993;7(4):357–360. 25. Hua KL, Hsu CH, Hidayati SC, et al. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther . 2015;8:2015–2022. 26. Iannuzzi NP, Leopold SS. In brief: the Mason classification of radial head fractures. Clin Orthop Relat Res . 2012;470(6):1799–1802. 27. Johnson JP, Karam M, Schisel J, Agel J. An evaluation of the OTA-OFC system in clinical practice: a multi-center study with 90 days outcomes. J Orthop Trauma . 2016;30(11):579–583. 28. Kenawey M. MRI Evaluation of the posterior pelvic bony and soft tissue injuries with tile C displaced pelvic fractures in young children. J Pediatr Orthop . 2020;40(7):e579–e586. 29. Knutsson SB, Wennergren D, Bojan A, et al. Femoral fracture classification in the Swedish Fracture Register: a validity study. BMC Musculoskelet Disord . 2019;20(1):197. 30. Koo H, Leveridge M, Thompson C, et al. Interobserver reliability of the young- burgess and tile classification systems for fractures of the pelvic ring. J Orthop Trauma . 2008;22(6):379–384. 31. Kooi T, Litjens G, van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal . 2017;35:303–312. 32. Kristiansen B, Andersen UL, Olsen CA, Varmarken JE. The Neer classification of fractures of the proximal humerus: an assessment of interobserver variation. Skeletal Radiol . 1988;17(6):420–422. 33. Langerhuizen DWG, Bulstra AEJ, Janssen SJ, et al. Is deep learning on par with human observers for detection of radiographically visible and occult fractures of the scaphoid? Clin Orthop Relat Res . 2020;478(11):2653–2659. 34. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature . 2015;521(7553):436–444. 35. Marmor MT, Agel J, Dumpe J, et al. Comparison of the Neer classification to the 2018 update of the Orthopedic Trauma Association/AO fracture classification for classifying proximal humerus fractures. OTA Int . 2022;5(3):e200. 36. Mason ML. Some observations on fractures of the head of the radius with a review of one hundred cases. Br J Surg . 1954;42(172):123–132. 37. McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) . 2012; 22(3):276–282. 38. Meinberg E, Agel J, Roberts C, et al. Fracture and Dislocation Classification Compendium–2018. Journal of Orthopaedic Trauma . Volume 32: Number 1; Supple ment, January 2018. 39. Misselyn D, Nijs S, Fieuws S, et al. Improved interobserver reliability of the Sanders classification in calcaneal fractures using segmented three-dimensional prints. J Foot Ankle Surg . 2018;57(3):440–444. 40. Nandi S. Revisiting Pauwels’ classification of femoral neck fractures. World J Orthop . 2021;12(11):811–815. 41. Neer CS 2nd. Displaced proximal humeral fractures. I. Classification and evaluation. J Bone Joint Surg Am . 1970;52(6):1077–1089. 42. Oestern HJ, Tscherne H. Pathophysiology and classification of soft tissue damage in fractures. Orthopade . 1983;12(1):2–8. 43. Olczak J, Emilson F, Razavian A, et al. Ankle fracture classification using deep learn ing: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification. Acta Orthop . 2021;92(1):102–108. 44. Orthopaedic Trauma Association Committee for Coding and Classification. Fracture and dislocation compendium. J Orthop Trauma . 1996;10(suppl 1):1–154.

Sanders R, Vaupel Z, Erdogan M, Downes K. The operative treatment of displaced intra-articular calcaneal fractures (DIACFs): long term (10–20 years) results in 108 fractures using a prognostic CT classification. J Orthop Trauma. 2014;28(10):551–563. Valderrama-Molina CO, Estrada-Castrillon M, Hincapie JA, et al. Intra- and interobserver agreement on the Oestern and Tscherne classification of soft tissue injury in periarticular lower-limb closed fractures. Colomb Med ( Cali ). 2014;45(4):173–178. REFERENCES 1. Agel J, Evans AR, Marsh JL, et al. The OTA open fracture classification: a study of reliability and agreement. J Orthop Trauma . 2013;27(7):379–384. 2. Agel J, Rockwood T, Barber R, et al. Potential predictive ability of the orthopae dic trauma association open fracture classification. J Orthop Trauma . 2014;28(5): 300–306. 3. Anderson DD, Mosqueda T, Thomas T, et al. Quantifying tibial plafond fracture severity: absorbed energy and fragment displacement agree with clinical rank order ing. J Orthop Res . 2008;26(8):1046–1052. 4. Beebe MJ, Auston DA, Quade JH, et al. OTA/AO classification is highly predictive of acute compartment syndrome after tibia fracture: a cohort of 2885 fractures. J Orthop Trauma . 2017;31(11):600–605. 5. Brumback RJ, Jones AL. Interobserver agreement in the classification of open frac tures of the tibia. The results of a survey of two hundred and forty-five orthopaedic surgeons. J Bone Joint Surg Am . 1994;76(8):1162–1166. 6. Busch U. 110 years ago: Wilhelm Conrad Roentgen received the first Nobel Prize. Z Med Phys . 2011;21(3):159–160. 7. Chung SW, Han SS, Lee JW, et al. Automated detection and classification of the prox imal humerus fracture by using deep learning algorithm. Acta Orthop . 2018;89(4): 468–473. 8. Colles A. Historical paper on the fracture of the carpal extremity of the radius (18). Injury . 1970;2(1):48–50. 9. Crosby LA, Fitzgibbons T. Computerized tomography scanning of acute intra- articular fractures of the calcaneus: a new classification system. J Bone Joint Surg Am . 1990;72(6):852–859. 10. Csizy M, Buckley R, Tough S, et al. Displaced intra-articular calcaneal fractures: vari ables predicting late subtalar fusion. J Orthop Trauma . 2003;17(2):106–112. 11. DeCoster TA, Willis MC, Marsh JL, et al. Rank order analysis of tibial plafond fractures: does injury or reduction predict outcome? Foot Ankle Int . 1999;20(1): 44–49. 12. Essex-Lopresti P. The mechanism, reduction technique, and results in fractures of the os calcis. Br J Surg . 1952;39(157):395–419. 13. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature . 2017;542(7639):115–118. 14. Garden RS, Mitchell WR. The treatment of cervical fractures of the femur [Resume]. Proc R Soc Med . 1959;52(10):866. 15. Gardner MJ, Demetrakopoulos D, Briggs SM, et al. The ability of the Lauge-Hansen classification to predict ligament injury and mechanism in ankle fractures: an MRI study. J Orthop Trauma . 2006;20(4):267–272. 16. Garner MR, Sethuraman SA, Schade MA, Boateng H. Antibiotic prophylaxis in open fractures: evidence, evolving issues, and recommendations. J Am Acad Orthop Surg . 2020;28(8):309–315. 17. Garner MR, Warner SJ, Heiner JA, et al. Evaluation of the orthopaedic trauma associ ation open fracture classification (OTA-OFC) as an outcome prediction tool in open tibial shaft fractures. Arch Orthop Trauma Surg . 2022;142(12):3599–3603. 18. Gary JL, Mulligan M, Banagan K, et al. Magnetic resonance imaging for the evalua tion of ligamentous injury in the pelvis: a prospective case-controlled study. J Orthop Trauma . 2014;28(1):41–47. 19. Ghoshal A, Enninghorst N, Sisak K, Balogh ZJ. An interobserver reliability compar ison between the Orthopaedic Trauma Association’s open fracture classification and the Gustilo and Anderson classification. Bone Joint J . 2018;100-B(2):242–246. 20. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA . 2016;316(22):2402–2410. 21. Gustilo RB, Anderson JT. Prevention of infection in the treatment of one thousand and twenty-five open fractures of long bones: retrospective and prospective analyses. J Bone Joint Surg Am . 1976;58(4):453–458.