A personalized approach to the treatment of traumatic spinal injuries: rationale, basic concept, and potential methods of implementation
DOI:
https://doi.org/10.25305/unj.325812Keywords:
traumatic spinal injuries, personalized approach, diagnosis-oriented strategy, clinical and economic effectiveness, prospects for healthcare developmentAbstract
Traumatic spinal injuries (TSIs) are a leading cause of disability and represent a significant socio-economic burden. Despite advancements in diagnostic and surgical techniques, treatment outcomes remain inconsistent. Standardized protocols often fail to account for individual patient characteristics, which can reduce the effectiveness of interventions and increase the risk of complications. This highlights the growing relevance of adopting individualized approaches in the treatment of TSIs.
Objective: To comprehensively analyze the economic, legal, clinical, and deontological aspects of implementing individualized approaches to the treatment of TSIs.
Materials and methods: An analytical literature review was conducted in accordance with the PRISMA protocol. Sources were selected from international scientific databases over the past 10 years using relevant MeSH terms.
Results: The literature review revealed that, despite technological advances, treatment outcomes in TSIs do not always improve proportionally with increased healthcare spending, illustrating the phenomenon of diminishing returns. The use of the QALY metric in several countries enables the evaluation of the cost-effectiveness of medical interventions; however, it has ethical limitations and is not yet implemented in Ukraine. The domestic Health Technology Assessment (HTA) system, introduced in 2020, does not currently include mandatory protocols for managing TSIs due to clinical heterogeneity, resource constraints, and legal risks. Standardized, diagnosis-driven protocols focused on the “average patient” often disregard individual variability, potentially leading to both overtreatment and undertreatment. Simplified injury classification systems enhance standardization but may reduce clinical decision-making accuracy in atypical cases. Furthermore, limited public understanding of evidence-based medicine contributes to ethical and communicative challenges. These findings underscore the importance of individualized approaches in TSI management.
Conclusions: Individualization of TSI treatment represents a logical extension of evidence-based medicine and promotes optimization of outcomes. It allows for flexible, patient-specific therapeutic strategies, improves the efficiency of healthcare resource utilization, and reduces complication rates. The ongoing development of analytical tools offers promising prospects for constructing personalized algorithms for managing highly heterogeneous patient populations.
References
1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (London, England). 2020;396(10258):1204-1222. [CrossRef] [PubMed]
2. Williams AL, Al-Busaidi A, Sparrow PJ, Adams JE, Whitehouse RW. Under-reporting of osteoporotic vertebral fractures on computed tomography. Eur J Radiol. 2009;69(1):179-183. [CrossRef] [PubMed]
3. Cause of death or injury. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2021 (GBD 2021) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME); 2022. https://vizhub.healthdata.org/gbd-results/
4. Sandean D. Management of acute spinal cord injury: A summary of the evidence pertaining to the acute management, operative and non-operative management. World J Orthop. 2020;11(12):573-583. [CrossRef] [PubMed]
5. Peterson MD, Meade MA, Lin P, Kamdar N, Rodriguez G, Krause JS, et al. Psychological morbidity following spinal cord injury and among those without spinal cord injury: the impact of chronic centralized and neuropathic pain. Spinal Cord. 2022;60(2):163-169. [CrossRef] [PubMed]
6. Limthongkul W, Singhatanadgige W, Vaccaro AR, Albert TJ, Radcliff K. Health-related quality of life and cost after cervical spine trauma. Seminars in Spine Surgery. 2014;26(1):30-37. [CrossRef]
7. Fiani B, Noblett C, Nanney J, Doan T, Pennington E, Jarrah R, et al. Diffusion tensor imaging of the spinal cord status post trauma. Surg Neurol Int. 2020;11:276. [PubMed]
8. Fatehi D, Dayani MA, Rostamzadeh A. Role of CT scan in theranostic and management of traumatic spinal cord injury. Saudi J Biol Sci. 2018;25(4):739-746. [CrossRef] [PubMed]
9. Ordonez CA, Parra MW, Holguin A, Garcia C, Guzman-Rodriguez M, Padilla N, et al. Whole-body computed tomography is safe, effective and efficient in the severely injured hemodynamically unstable trauma patient. Colomb Med (Cali). 2020;51(4):e4054362. [CrossRef] [PubMed]
10. Seif M, Curt A, Thompson AJ, Grabher P, Weiskopf N, Freund P. Quantitative MRI of rostral spinal cord and brain regions is predictive of functional recovery in acute spinal cord injury. Neuroimage Clin. 2018;20:556-563. [CrossRef] [PubMed]
11. Aly MM, Al-Shoaibi AM, Aljuzair AH, Issa TZ, Vaccaro AR. A Proposal for a Standardized Imaging Algorithm to Improve the Accuracy and Reliability for the Diagnosis of Thoracolumbar Posterior Ligamentous Complex Injury in Computed Tomography and Magnetic Resonance Imaging. Global Spine J. 2023;13(3):873-896. [CrossRef] [PubMed]
12. Chuang CH, Huang CY, Ho SW, Chen CC. Rapid Detecting Brachial Plexus Injury by Point-of-Care Ultrasonography. J Med Ultrasound. 2022;30(4):303-305. [CrossRef] [PubMed]
13. Vk V, Bhoi S, Aggarwal P, Murmu LR, Agrawal D, Kumar A, et al. Diagnostic utility of point of care ultrasound in identifying cervical spine injury in emergency settings. Australas J Ultrasound Med. 2021;24(4):208-216. [CrossRef] [PubMed]
14. Taylor C, Metcalf A, Morales A, Lam J, Wilson R, Baribeault T. Multimodal Analgesia and Opioid-Free Anesthesia in Spinal Surgery: A Literature Review. J Perianesth Nurs. 2023;38(6):938-942. [CrossRef] [PubMed]
15. Yang S, Xiao W, Wu H, Liu Y, Feng S, Lu J, et al. Management Based on Multimodal Brain Monitoring May Improve Functional Connectivity and Post-operative Neurocognition in Elderly Patients Undergoing Spinal Surgery. Front Aging Neurosci. 2021;13:705287. [CrossRef] [PubMed]
16. Avrumova F, Lebl DR. Augmented reality for minimally invasive spinal surgery. Front Surg. 2022;9:1086988. [CrossRef] [PubMed]
17. Walker CT, Xu DS, Godzik J, Turner JD, Uribe JS, Smith WD. Minimally invasive surgery for thoracolumbar spinal trauma. Ann Transl Med. 2018;6(6):102. [CrossRef] [PubMed]
18. Ding W, Hu S, Wang P, Kang H, Peng R, Dong Y, et al. Spinal Cord Injury: The Global Incidence, Prevalence, and Disability From the Global Burden of Disease Study 2019. Spine (Phila Pa 1976). 2022;47(21):1532-1540. [CrossRef] [PubMed]
19. Morone G, Pirrera A, Iannone A, Giansanti D. Development and Use of Assistive Technologies in Spinal Cord Injury: A Narrative Review of Reviews on the Evolution, Opportunities, and Bottlenecks of Their Integration in the Health Domain. Healthcare (Basel). 2023 Jun 4;11(11):1646. [CrossRef] [PubMed] [PubMed Central]
20. Sammon JD, Abdollah F, Klett DE, Pucheril D, Sood A, Trinh QD, et al. The diminishing returns of robotic diffusion: complications after robot-assisted radical prostatectomy. BJU Int. 2016;117(2):211-212. [CrossRef] [PubMed]
21. Passias PG, Bortz C, Horn SR, Segreto FA, Stekas N, Ge DH, et al. Diminishing Clinical Returns of Multilevel Minimally Invasive Lumbar Interbody Fusion. Spine (Phila Pa 1976). 2019;44(20):E1181-E1187. [CrossRef] [PubMed]
22. Gold MR, Siegel JE, Russell LB, Weinstein MC, editors. Cost-Effectiveness in Health and Medicine. New York: Oxford Academic; 1996. [CrossRef]
23. Dietz N, Sharma M, Alhourani A, Ugiliweneza B, Wang D, Nuno MA, et al. Variability in the utility of predictive models in predicting patient-reported outcomes following spine surgery for degenerative conditions: a systematic review. Neurosurg Focus. 2018;45(5):E10. [CrossRef] [PubMed]
24. Toure M, Kouakou CRC, Poder TG. Dimensions Used in Instruments for QALY Calculation: A Systematic Review. Int J Environ Res Public Health. 2021;18(9). [CrossRef] [PubMed]
25. Lotfi R, Haqiqat E, Sadra Rajabi M, Hematyar A. Robust and resilience budget allocation for projects with a risk-averse approach: A case study in healthcare projects. Computers and Industrial Engineering. 2023 Feb;176:108948. [CrossRef]
26. Siverskog J, Henriksson M. On the role of cost-effectiveness thresholds in healthcare priority setting. Int J Technol Assess Health Care. 2021;37:e23. [CrossRef] [PubMed]
27. Iino H, Hashiguchi M, Hori S. Estimating the range of incremental cost-effectiveness thresholds for healthcare based on willingness to pay and GDP per capita: A systematic review. PLoS One. 2022;17(4):e0266934. [CrossRef] [PubMed]
28. Petrou S. Methodological challenges surrounding QALY estimation for paediatric economic evaluation. Cost Eff Resour Alloc. 2022;20(1):10. [CrossRef] [PubMed]
29. Towse A, Garau M. Appraising ultra-orphan drugs: is cost-per-QALY appropriate? A review of the evidence. Consulting Report. 2018 Mar 1(001978). https://www.ohe.org/wp-content/uploads/2018/03/468-Appraising-ultra-orphan-drugs.pdf
30. Use of QALYs: advantages and concerns. PharmacoEconomics & Outcomes News. 2018;806(1):2-2. [CrossRef]
31. Pollard D, Fuller G, Goodacre S, van Rein EAJ, Waalwijk JF, van Heijl M. An economic evaluation of triage tools for patients with suspected severe injuries in England. BMC Emerg Med. 2022;22(1):4. [CrossRef] [PubMed]
32. Cabinet of Ministers of Ukraine. Resolution of December 23, 2020 No. 1300 "On Approval of the Procedure for Conducting State Assessment of Medical Technologies." Kyiv: Cabinet of Ministers of Ukraine; 2020. https://zakon.rada.gov.ua/laws/show/1300-2020-%D0%BF#Text
33. Greenberg JK, Burks SS, Dibble CF, Javeed S, Gupta VP, Yahanda AT, et al. An updated management algorithm for incorporating minimally invasive techniques to treat thoracolumbar trauma. J Neurosurg Spine. 2022;36(4):558-567. [CrossRef] [PubMed]
34. Portelli Tremont JN, Cook N, Murray LH, Udekwu PO, Motameni AT. Acute Traumatic Spinal Cord Injury: Implementation of a Multidisciplinary Care Pathway. J Trauma Nurs. 2022;29(4):218-224. [CrossRef] [PubMed]
35. O’Toole JE, Kaiser MG, Anderson PA, Arnold PM, Chi JH, Dailey AT, et al. Congress of Neurological Surgeons Systematic Review and Evidence-Based Guidelines on the Evaluation and Treatment of Patients with Thoracolumbar Spine Trauma: Executive Summary. Neurosurgery. 2019;84(1):2-6. [CrossRef] [PubMed]
36. Bolcato M, Fassina G, Rodriguez D, Russo M, Aprile A. The contribution of legal medicine in clinical risk management. BMC Health Serv Res. 2019;19(1):85. [CrossRef] [PubMed]
37. Agha L, Frandsen B, Rebitzer JB. Fragmented division of labor and healthcare costs: Evidence from moves across regions. Journal of Public Economics. 2019;169:144-159. [CrossRef]
38. Kellner DB, Urman RD, Greenberg P, Brovman EY. Analysis of adverse outcomes in the post-anesthesia care unit based on anesthesia liability data. J Clin Anesth. 2018;50:48-56. [CrossRef] [PubMed]
39. Corte-Real A, Caetano C, Alves S, Pereira AD, Rocha S, Nuno Vieira D. Patient Safety in Dental Practice: Lessons to Learn About the Risks and Limits of Professional Liability. Int Dent J. 2021;71(5):378-383. [CrossRef] [PubMed]
40. Akmal A, Podgorodnichenko N, Foote J, Greatbanks R, Stokes T, Gauld R. Why is Quality Improvement so Challenging? A Viable Systems Model Perspective to Understand the Frustrations of Healthcare Quality Improvement Managers. Health Policy. 2021;125(5):658-664. [CrossRef] [PubMed]
41. Arntsen B, Torjesen DO, Karlsen TI. Asymmetry in inter-municipal cooperation in health services - How does it affect service quality and autonomy? Soc Sci Med. 2021;273:113744. [CrossRef] [PubMed]
42. Sittig DF, Belmont E, Singh H. Improving the safety of health information technology requires shared responsibility: It is time we all step up. Healthc (Amst). 2018;6(1):7-12. [CrossRef] [PubMed]
43. Vilchyk TВ, Krainyk НS, Shandula OO. Legal enforcement and development directions of health law in Ukraine. Wiad Lek. 2019;72(4):692-696. [CrossRef] [PubMed]
44. Guest JD, Kelly-Hedrick M, Williamson T, Park C, Ali DM, Sivaganesan A, et al. Development of a Systems Medicine Approach to Spinal Cord Injury. J Neurotrauma. 2023;40(17-18):1849-1877. [CrossRef] [PubMed]
45. Gameiro GR, Sinkunas V, Liguori GR, Auler-Junior JOC. Precision Medicine: Changing the way we think about healthcare. Clinics (Sao Paulo, Brazil). 2018;73:e723. [CrossRef] [PubMed]
46. Tian C, Lv Y, Li S, Wang DD, Bai Y, Zhou F, et al. Factors related to improved American Spinal Injury Association grade of acute traumatic spinal cord injury. World J Clin Cases. 2020;8(20):4807-4815. [CrossRef] [PubMed]
47. Vaccaro AR, Oner C, Kepler CK, Dvorak M, Schnake K, Bellabarba C, et al. AOSpine thoracolumbar spine injury classification system: fracture description, neurological status, and key modifiers. Spine (Phila Pa 1976). 2013;38(23):2028-2037. [CrossRef] [PubMed]
48. Vaccaro AR, Lehman RA, Jr., Hurlbert RJ, Anderson PA, Harris M, Hedlund R, et al. A new classification of thoracolumbar injuries: the importance of injury morphology, the integrity of the posterior ligamentous complex, and neurologic status. Spine (Phila Pa 1976). 2005;30(20):2325-2333. [CrossRef] [PubMed]
49. Canseco JA, Schroeder GD, Paziuk TM, Karamian BA, Kandziora F, Vialle EN, et al. The Subaxial Cervical AO Spine Injury Score. Global Spine J. 2022;12(6):1066-1073. [CrossRef] [PubMed]
50. Verheyden AP, Spiegl UJ, Ekkerlein H, Gercek E, Hauck S, Josten C, et al. Treatment of Fractures of the Thoracolumbar Spine: Recommendations of the Spine Section of the German Society for Orthopaedics and Trauma (DGOU). Global Spine J. 2018;8(2 Suppl):34S-45S. [CrossRef] [PubMed]
51. Nilsbakken IMW, Sollid S, Wisborg T, Jeppesen E. Assessing Trauma Management in Urban and Rural Populations in Norway: A National Register-Based Research Protocol. JMIR Res Protoc. 2022;11(6):e30656. [CrossRef] [PubMed]
52. Wuthisuthimethawee P, Sookmee W, Damnoi S. Non-randomized comparative study on the efficacy of a trauma protocol in the emergency department. Chinese journal of traumatology = Zhonghua chuang shang za zhi. 2019;22(4):207-211. [CrossRef] [PubMed]
53. Jureidini J, McHenry LB. The illusion of evidence based medicine. BMJ (Clinical research ed). 2022;376:o702. [CrossRef] [PubMed]
54. Sadeghi-Bazargani H, Tabrizi JS, Azami-Aghdash S. Barriers to evidence-based medicine: a systematic review. J Eval Clin Pract. 2014;20(6):793-802. [CrossRef] [PubMed]
55. Every-Palmer S, Howick J. How evidence-based medicine is failing due to biased trials and selective publication. J Eval Clin Pract. 2014;20(6):908-914. [CrossRef] [PubMed]
56. Ioannidis JP. Why Most Clinical Research Is Not Useful. PLoS Med. 2016;13(6):e1002049. [CrossRef] [PubMed]
57. Greenhalgh T, Howick J, Maskrey N, Evidence Based Medicine Renaissance G. Evidence based medicine: a movement in crisis? BMJ (Clinical research ed). 2014;348:g3725. [CrossRef] [PubMed]
58. Kwak SG, Kim JH. Central limit theorem: the cornerstone of modern statistics. Korean J Anesthesiol. 2017;70(2):144-156. [CrossRef] [PubMed]
59. Alimohammadi E, Bagheri SR, Joseph B, Sharifi H, Shokri B, Khodadadi L. Analysis of factors associated with the failure of treatment in thoracolumbar burst fractures treated with short-segment posterior spinal fixation. Journal of orthopaedic surgery and research. 2023;18(1):690. [CrossRef] [PubMed]
60. Soultanis K, Thano A, Soucacos PN. "Outcome of thoracolumbar compression fractures following non-operative treatment". Injury. 2021;52(12):3685-3690. [CrossRef] [PubMed]
61. Bruckner J, Hashmi S, Williams SK, Ludwig S. Minimally invasive surgery for the management of thoracolumbar burst fractures. Seminars in Spine Surgery. 2021;33(1):100848. [CrossRef]
62. Cowley A, Nelson M, Hall C, Goodwin S, Kumar DS, Moore F. Recommendation for changes to the guidelines of trauma patients with potential spinal injury within a regional UK ambulance trust. Br Paramed J. 2022;7(3):59-67. [CrossRef] [PubMed]
63. Alfaro-Mico J, Ramirez-Villaescusa J, Martinez-Lozano MD, Sanchez-Honrubia RM, Ruiz-Picazo D. Emergency stabilisation by single-stage posterior transpedicular approach for treatment of unstable lumbar spine fracture with neurological injury. Trauma case reports. 2020;27:100300. [CrossRef] [PubMed]
64. Rogers JR, Pavisic J, Ta CN, Liu C, Soroush A, Kuen Cheung Y, et al. Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria’s impact on patient count and safety. J Biomed Inform. 2022;127:104032. [CrossRef] [PubMed]
65. Nouhi M, Hadian M, Olyaeemanesh A. The clinical and economic consequences of practice style variations in common surgical interventions: A protocol for systematic review. Medicine (Baltimore). 2018;97(42):e12439. [CrossRef] [PubMed]
66. Allen BL, Jr., Ferguson RL, Lehmann TR, O’Brien RP. A mechanistic classification of closed, indirect fractures and dislocations of the lower cervical spine. Spine (Phila Pa 1976). 1982;7(1):1-27. [CrossRef] [PubMed]
67. Magerl F, Aebi M, Gertzbein SD, Harms J, Nazarian S. A comprehensive classification of thoracic and lumbar injuries. Eur Spine J. 1994;3(4):184-201. [CrossRef] [PubMed]
68. Nekhlopochyn OS, Cheshuk YV. Traumatic injuries of the thoracolumbar junction. Classification by Friedrich P. Magerl et al. Trauma. 2022;23(3):4-22. [CrossRef]
69. Marsh JL, Slongo TF, Agel J, Broderick JS, Creevey W, DeCoster TA, et al. Fracture and dislocation classification compendium - 2007: Orthopaedic Trauma Association classification, database and outcomes committee. J Orthop Trauma. 2007;21(10 Suppl):S1-133. [CrossRef] [PubMed]
70. Divi SN, Schroeder GD, Oner FC, Kandziora F, Schnake KJ, Dvorak MF, et al. AOSpine-Spine Trauma Classification System: The Value of Modifiers: A Narrative Review With Commentary on Evolving Descriptive Principles. Global Spine J. 2019;9(1 Suppl):77S-88S. [CrossRef] [PubMed]
71. Vaccaro AR, Koerner JD, Radcliff KE, Oner FC, Reinhold M, Schnake KJ, et al. AOSpine subaxial cervical spine injury classification system. Eur Spine J. 2016;25(7):2173-2184. [CrossRef] [PubMed]
72. Azimi P, Mohammadi HR, Azhari S, Alizadeh P, Montazeri A. The AOSpine thoracolumbar spine injury classification system: A reliability and agreement study. Asian journal of neurosurgery. 2015;10(4):282-285. [CrossRef] [PubMed]
73. Lopes FAR, Ferreira A, Santos R, Macaneiro CH. Intraobserver and interobserver reproducibility of the old and new classifications of toracolombar fractures. Rev Bras Ortop. 2018;53(5):521-526. [CrossRef] [PubMed]
74. Sriganesh K, Shanthanna H, Busse JW. A brief overview of systematic reviews and meta-analyses. Indian journal of anaesthesia. 2016;60(9):689-694. [CrossRef] [PubMed]
75. Vaccaro AR, Schroeder GD, Kepler CK, Cumhur Oner F, Vialle LR, Kandziora F, et al. The surgical algorithm for the AOSpine thoracolumbar spine injury classification system. Eur Spine J. 2016;25(4):1087-1094. [CrossRef] [PubMed]
76. Pishnamaz M, Balosu S, Curfs I, Uhing D, Laubach M, Herren C, et al. Reliability and Agreement of Different Spine Fracture Classification Systems: An Independent Intraobserver and Interobserver Study. World Neurosurg. 2018;115:e695-e702. [CrossRef] [PubMed]
77. Berkman ND, Lohr KN, Ansari MT, Balk EM, Kane R, McDonagh M, et al. Grading the strength of a body of evidence when assessing health care interventions: an EPC update. J Clin Epidemiol. 2015;68(11):1312-1324. [CrossRef] [PubMed]
78. Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ (Clinical research ed). 2018;363:k4245. [CrossRef] [PubMed]
79. Gugiu PC. Hierarchy of evidence and appraisal of limitations (HEAL) grading system. Eval Program Plann. 2015;48:149-159. [CrossRef] [PubMed]
80. Ioannidis JPA. Hijacked evidence-based medicine: stay the course and throw the pirates overboard. J Clin Epidemiol. 2017;84:11-13. [CrossRef] [PubMed]
81. Keane M, Berg C. Evidence-based medicine: A predictably flawed paradigm. Trends in Anaesthesia and Critical Care. 2016;9:49-52. [CrossRef]
82. Tebala GD. The Emperor’s New Clothes: a Critical Appraisal of Evidence-based Medicine. Int J Med Sci. 2018;15(12):1397-1405. [CrossRef] [PubMed]
83. Roland D. Social Media, Health Policy, and Knowledge Translation. J Am Coll Radiol. 2018;15(1 Pt B):149-152. [CrossRef] [PubMed]
84. Jeyaraman M, Ramasubramanian S, Kumar S, Jeyaraman N, Selvaraj P, Nallakumarasamy A, et al. Multifaceted Role of Social Media in Healthcare: Opportunities, Challenges, and the Need for Quality Control. Cureus. 2023;15(5):e39111. [CrossRef] [PubMed]
85. Dijkstra S, Kok G, Ledford JG, Sandalova E, Stevelink R. Possibilities and Pitfalls of Social Media for Translational Medicine. Front Med (Lausanne). 2018;5:345. [CrossRef] [PubMed]
86. Jacobs R, Prabhu AV, Monaco EA, Tonetti D, Agarwal N. Patient perception of gamma knife stereotactic radiosurgery through twitter and instagram. Interdisciplinary Neurosurgery. 2018;13:138-140. [CrossRef]
87. Forgie EME, Lai H, Cao B, Stroulia E, Greenshaw AJ, Goez H. Social Media and the Transformation of the Physician-Patient Relationship: Viewpoint. J Med Internet Res. 2021;23(12):e25230. [CrossRef] [PubMed]
88. Boushehri E, Soltani Arabshahi K, Monajemi A. Clinical reasoning assessment through medical expertise theories: past, present and future directions. Med J Islam Repub Iran. 2015;29:222. [PubMed]
89. Shah HM, Chung KC. Archie Cochrane and his vision for evidence-based medicine. Plast Reconstr Surg. 2009;124(3):982-988. [CrossRef] [PubMed]
90. Cochrane A. Effectiveness and Efficiency: Random Reflections on Health Services. London: Nuffield Provincial Hospitals Trust; 1972. https://www.nuffieldtrust.org.uk/research/effectiveness-and-efficiency-random-reflections-on-health-services
91. Gerris J. The legacy of Archibald Cochrane: from authority based towards evidence based medicine. Facts Views Vis Obgyn. 2011;3(4):233-237. [PubMed]
92. Askheim C, Sandset T, Engebretsen E. Who cares? The lost legacy of Archie Cochrane. Med Humanit. 2017;43(1):41-46. [CrossRef] [PubMed]
93. Arjas E, Gasbarra D. Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective. BMC Med Res Methodol. 2022;22(1):50. [CrossRef] [PubMed]
94. Williamson SF, Jacko P, Villar SS, Jaki T. A Bayesian adaptive design for clinical trials in rare diseases. Comput Stat Data Anal. 2017;113:136-153. [CrossRef] [PubMed]
95. Edlitz Y, Segal E. Prediction of type 2 diabetes mellitus onset using logistic regression-based scorecards. Elife. 2022;11. [CrossRef] [PubMed]
96. Fukunishi H, Nishiyama M, Luo Y, Kubo M, Kobayashi Y. Alzheimer-type dementia prediction by sparse logistic regression using claim data. Comput Methods Programs Biomed. 2020;196:105582. [CrossRef] [PubMed]
97. van Walraven C, McAlister FA. Competing risk bias was common in Kaplan-Meier risk estimates published in prominent medical journals. J Clin Epidemiol. 2016;69:170-173 e178. [CrossRef] [PubMed]
98. Hu C, Cao J, Zeng L, Luo Y, Fan H. Prognostic factors for squamous cervical carcinoma identified by competing-risks analysis: A study based on the SEER database. Medicine (Baltimore). 2022;101(39):e30901. [CrossRef] [PubMed]
99. Khan O, Badhiwala JH, Grasso G, Fehlings MG. Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care. World Neurosurg. 2020;140:512-518. [CrossRef] [PubMed]
100. Inoue T, Ichikawa D, Ueno T, Cheong M, Inoue T, Whetstone WD, et al. XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury. Neurotrauma Rep. 2020;1(1):8-16. [CrossRef] [PubMed]
101. Langarizadeh M, Moghbeli F. Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review. Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH. 2016;24(5):364-369. [CrossRef] [PubMed]
102. Vemulapalli V, Qu J, Garren JM, Rodrigues LO, Kiebish MA, Sarangarajan R, et al. Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data. Artif Intell Med. 2016;74:1-8. [CrossRef] [PubMed]
103. Khanmohammadi S, Adibeig N, Shanehbandy S. An improved overlapping k-means clustering method for medical applications. Expert Systems with Applications. 2017;67:12-18. [CrossRef]
104. Zhu H, He H, Xu J, Fang Q, Wang W. Medical Image Segmentation Using Fruit Fly Optimization and Density Peaks Clustering. Comput Math Methods Med. 2018;2018:3052852. [CrossRef] [PubMed]
105. Kocbek P, Fijacko N, Soguero-Ruiz C, Mikalsen KO, Maver U, Povalej Brzan P, et al. Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data. Comput Math Methods Med. 2019;2019:2059851. [CrossRef] [PubMed]
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