Accuracy of Pediatric Prehospital Triage Systems in Predicting Critical Outcomes: A Systematic Review with Narrative Synthesis

Accuracy of Pediatric Prehospital Triage Systems in Predicting Critical Outcomes: A Systematic Review with Narrative Synthesis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Accuracy of Pediatric Prehospital Triage Systems in Predicting Critical Outcomes: A Systematic Review with Narrative Synthesis Aydin Mahmoud Alilou, Jafar Khani, Reza Rostami, Milad Chinehkesh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8126290/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Accurate prehospital triage of injured children is essential to ensure timely care while minimizing resource misuse. Various pediatric-specific and adapted adult triage tools (e.g. JumpSTART [ 3 ], Pediatric Triage Tape [ 2 ], MPTT-P/BCD sieve [ 4 , 5 ]) have been proposed, but their ability to predict critical outcomes – including life-saving interventions (LSI), major trauma (e.g. ISS > 15), ICU admission, and mortality – remains unclear. We performed a PRISMA-compliant systematic review and meta-analysis of studies (2015–2024) evaluating the diagnostic accuracy (sensitivity, specificity, under/over-triage) of prehospital pediatric triage systems in trauma and mass-casualty settings. Methods We searched PubMed, Embase, and Scopus (2015–2024) for peer-reviewed studies of pediatric ( 15, death). We extracted data on sensitivity, specificity, over- and under-triage rates, and outcome definitions. Methodological quality was assessed with QUADAS-2 [ 14 ]. Pooled estimates of sensitivity/specificity were calculated using random-effects models. A PRISMA flowchart (Fig. 1) summarizes study selection. Results We included 20 studies evaluating tools such as JumpSTART [ 3 ], PTT [ 2 ], Smart/START, SALT [ 3 ], the Sheffield Paediatric Triage Tool (SPTT) [ 5 ], the Battlefield Casualty Drills (BCD) sieve [ 4 ], and adapted adult tools (e.g. MPTT-24 [ 5 ]). van der Sluijs et al. (2018) reported field triage sensitivities ranging 49–87% (none reached the ideal ≥ 95%) [ 18 ]. In a UK registry, only 2 of 11 pediatric tools achieved < 5% under-triage, but with very high over-triage (72–83%) [ 19 ]. Mass-casualty simulations showed modest accuracy: SALT (59% correct, under-triage 33%) and JumpSTART (57%, under-triage 39%) performed comparably [ 3 ]. Child-focused tools often under-performed relative to adult tools: e.g. JumpSTART sensitivity was 28–35% in 0–8y olds, vs. adult START ≈ 60% [ 4 ]. Conversely, novel adaptations showed promise: the SPTT achieved 92.2% sensitivity but with 75% over-triage [ 5 ]. The adult BCD triage sieve had the highest sensitivity (75.7%) for P1 status [ 4 ], exceeding PTT performance. Conclusion Pediatric-specific triage tools like JumpSTART [ 3 ] and PTT [ 2 ] tend to under-triage, while adult-adapted tools (e.g. MPTT-24, BCD sieve, SPTT [ 4 , 5 ]) improve sensitivity at the expense of higher over-triage. No tool fully meets benchmark standards [ 1 , 8 ]. These findings support revising pediatric triage strategies and testing enhanced tools. in Emergency Nursing Department of Emergency Nursing School of Nursing and Midwifery University of Medical Sciences Tabriz Iran Figures Figure 1 Figure 2 Introduction Traumatic injury remains a leading cause of death and disability among children worldwide. In both high-income and low-resource settings, children are frequently exposed to unintentional trauma from mechanisms such as motor vehicle collisions, falls, and violence, often resulting in significant morbidity or mortality [ 1 , 13 ]. The effective management of pediatric trauma relies on a coordinated chain of care, beginning with prehospital triage. The primary goal of prehospital triage is to promptly identify patients who require immediate life-saving interventions (LSIs), intensive care, or urgent surgical management, and to ensure they are transported to appropriate healthcare facilities in a timely fashion [ 1 , 6 ]. Unlike adults, children present unique challenges during triage due to their variable physiology, age-specific vital signs, and communication limitations. Their capacity to physiologically compensate until sudden decompensation makes early recognition of serious injury difficult [ 9 , 13 ]. Furthermore, the emotional and behavioral variability of children complicates rapid assessment in chaotic or high-pressure environments. Therefore, the development of pediatric-specific triage tools is essential to reduce both under-triage (missed critical injuries) and over-triage (misclassification of stable patients), which can respectively lead to preventable death or overburdened trauma systems [ 8 ]. Several triage algorithms have been adapted or developed for pediatric use in both routine trauma care and disaster scenarios. For example, JumpSTART was introduced as a pediatric counterpart to the adult START algorithm and incorporates child-specific respiratory and mental status criteria [ 3 ]. The Pediatric Triage Tape (PTT) uses length-based categorization to guide physiologic cutoffs more appropriate to children [ 2 ]. More recently, adult tools like the Modified Physiological Triage Tool (MPTT-24) and the Battlefield Casualty Drills (BCD) sieve have been applied to children, with varying success [ 4 ]. The Sheffield Paediatric Triage Tool (SPTT) has also emerged as a promising pediatric-focused model, although its validation remains limited to retrospective datasets [ 5 ]. Despite these innovations, no triage tool currently meets the benchmark sensitivity of ≥ 95% for identifying critical pediatric cases as recommended by trauma system guidelines [ 8 ]. Studies consistently show that both pediatric-specific and adapted adult tools demonstrate suboptimal accuracy: JumpSTART and PTT often under-triage severely injured children [ 2 , 3 ], while tools like SPTT or BCD sieve may increase sensitivity but at the cost of excessive over-triage [ 4 , 5 ]. The high stakes of pediatric trauma—combined with variability in outcome definitions (e.g., LSI, ISS > 15, ICU admission)—further complicate tool evaluation and clinical adoption [ 1 , 13 ]. Given this context, a comprehensive evaluation of the diagnostic accuracy of pediatric prehospital triage systems is critically needed. This systematic review and meta-analysis synthesizes peer-reviewed studies from 2015 to 2024 to assess how well current triage tools predict serious outcomes in children, including the need for LSIs, ICU admission, major trauma, or mortality. We focus on both general EMS applications and mass casualty incident (MCI) scenarios, aiming to clarify which triage models provide the most accurate, safe, and implementable strategies for pediatric populations. In doing so, we also highlight existing evidence gaps and opportunities for improved pediatric trauma care pathways. Methods Study Design This study was designed as a systematic review and meta-analysis following the PRISMA 2020 guidelines [ 12 ]. The protocol was developed before study initiation and followed a structured approach including study identification, screening, selection, data extraction, and synthesis. Data Sources and Search Strategy A systematic search was performed in PubMed , Embase , Scopus , and Web of Science to identify relevant studies published from January 1, 2015, through December 31, 2024 . We used both MeSH terms and keywords related to pediatric trauma and triage systems: “pediatric,” “prehospital triage,” “JumpSTART,” “Paediatric Triage Tape,” “SPTT,” “MPTT,” “BCD sieve,” “mass casualty,” “sensitivity,” and “specificity.” Boolean operators were applied to optimize retrieval. Additional records were identified through manual screening of reference lists [ 1 , 8 , 12 ]. Eligibility Criteria Inclusion criteria were: Peer-reviewed original studies published in English; Pediatric population (< 18 years); Assessment of a prehospital triage system (e.g., JumpSTART [ 3 ], PTT [ 2 ], MPTT-24 [ 5 ], SPTT [ 5 ], BCD sieve [ 4 ]); Reporting of diagnostic performance (sensitivity, specificity, over/under-triage); Outcome-based reference standard: LSI, ISS > 15, ICU admission, or mortality. Exclusion criteria included: Adult-only studies; In-hospital triage systems without prehospital relevance; No outcome-based reference; Editorials, conference abstracts, case reports, and reviews. Study Selection Search results were deduplicated and imported into Rayyan software. Two reviewers independently screened titles and abstracts. Discrepancies were resolved via consensus or a third reviewer. Full texts of selected articles were reviewed in detail. The screening process is illustrated in the PRISMA diagram (Fig. 1 ). Data Extraction For each eligible study, the following data were extracted using a standardized form: Study characteristics (author, year, setting, design); Sample size, patient age range; Triage tool used; Outcome definition and reference standard; Diagnostic accuracy measures (sensitivity, specificity, PPV, NPV, under-/over-triage rates). Risk of Bias Assessment We assessed study quality using QUADAS-2 [ 14 ], examining four domains: Patient selection, Index test, Reference standard, Flow and timing. Each was rated as “low,” “high,” or “unclear” risk of bias. Two independent reviewers conducted the assessments; disagreements were resolved through discussion. Data Synthesis and Analysis Due to heterogeneity across populations, outcomes, and tools, narrative synthesis was prioritized. Where ≥ 3 studies assessed a tool using comparable criteria, meta-analysis using a bivariate random-effects model was performed to calculate pooled sensitivity/specificity with 95% CIs. Forest plots were generated for key tools (Fig. 2 ). Study characteristics and outcomes were tabulated (Table 1 , Table 2 ). Analyses were conducted in RevMan 5.4 and MetaDTA . Statistical significance was defined as p < 0.05. Results Study Selection and Characteristics A total of 2,431 unique records were identified through electronic database searching. After removal of duplicates and screening of titles and abstracts, 84 full-text articles were assessed for eligibility. Of these, 20 studies met the inclusion criteria and were included in this systematic review (Fig. 1 ). These studies were published between 2015 and 2024 and were conducted in various settings, including the United States, United Kingdom, Australia, and multiple European countries. Most studies (n = 12) were retrospective cohort analyses using trauma registry data, while six were simulation-based evaluations and two were prospective observational studies. A summary of included study characteristics is presented in Table 1 . The pediatric populations assessed ranged from neonates to adolescents (< 18 years), and sample sizes varied significantly, ranging from fewer than 100 patients to large-scale national trauma datasets comprising over 30,000 children. The most commonly evaluated prehospital triage tools included JumpSTART [ 3 ], Paediatric Triage Tape (PTT) [ 2 ], the Modified Physiological Triage Tool (MPTT-24) [ 5 ], the Sheffield Paediatric Triage Tool (SPTT) [ 5 ], and the Battlefield Casualty Drills (BCD) sieve [ 4 ]. Additional tools included SALT [ 3 ], CareFlight [ 6 ], Smart [ 3 ], and regional triage algorithms. Diagnostic Accuracy in Routine Pediatric Trauma Diagnostic performance of triage tools varied widely. JumpSTART, although pediatric-specific, consistently under-triaged high-acuity patients, particularly in children under 8 years of age. Reported sensitivity ranged from 28% to 45% across studies [ 3 , 4 ]. PTT showed similarly poor performance, with sensitivity values as low as 34% in some cohorts [ 2 , 4 ]. The BCD sieve, although not specifically pediatric, achieved a sensitivity of 75.7% for Priority 1 classification in one large U.K.-based study, substantially outperforming both JumpSTART and PTT [ 4 ]. The MPTT-24, a physiology-based triage tool, showed strong performance across multiple studies, with reported sensitivity ranging from 72% to 81%, and specificity between 60% and 85% [ 5 ]. Its pediatric adaptation, the Sheffield Paediatric Triage Tool (SPTT), demonstrated an even higher sensitivity of 92.2% for life-saving interventions, although this was accompanied by an over-triage rate of 75% [ 5 ]. Specificity for most triage tools was highly variable and inversely related to sensitivity. Pediatric-specific algorithms tended to sacrifice sensitivity for specificity, failing to meet the American College of Surgeons’ recommended sensitivity threshold of ≥ 95% for trauma triage tools [ 1 , 8 ]. Over-triage rates for pediatric triage tools ranged from 33% to 83%, exceeding acceptable levels in most studies (Table 2 ). Diagnostic Accuracy in Mass Casualty Simulations Simulation-based studies evaluated tool performance in high-pressure, multi-casualty settings. A randomized simulation by Jones et al. compared SALT and JumpSTART, finding both had moderate diagnostic accuracy (~ 57–59%) and high under-triage rates (~ 33–39%) [ 3 ]. In large dataset analyses, CareFlight outperformed JumpSTART and START in predicting critical interventions, suggesting that some adult-modified tools may perform better than traditional pediatric triage algorithms [ 6 ]. In a comparative analysis of 11 triage tools using a national pediatric trauma registry, Vassallo et al. concluded that the SPTT was the most sensitive tool (92.2%) for identifying children requiring life-saving interventions [ 5 ]. However, the high over-triage rate (75%) highlighted the persistent trade-off between sensitivity and specificity in pediatric triage. Some tools, like the BCD sieve and MPTT-24, showed more balanced performance across settings and age groups, leading authors to recommend consideration of a unified age-independent physiologic triage strategy [ 4 , 5 ]. The variability in performance further emphasized the impact of population, setting, and tool complexity on outcomes. Quality Assessment of Included Studies Quality appraisal using QUADAS-2 revealed that 14 studies had low risk of bias across all domains, while six had some concerns. The most common methodological issues included retrospective classification of outcomes, unclear blinding of outcome assessors, and lack of standardized outcome definitions. Most studies clearly defined reference standards (e.g., ISS > 15, LSI criteria), enhancing applicability. Discussion This systematic review and meta-analysis comprehensively evaluated the diagnostic accuracy of pediatric prehospital triage tools in predicting critical outcomes, including the need for life-saving interventions (LSIs), major trauma (ISS > 15), intensive care unit (ICU) admission, and mortality. Our findings indicate that although numerous tools have been developed or adapted for pediatric use, none consistently meets the performance benchmarks recommended by trauma systems, particularly the sensitivity threshold of ≥ 95% as outlined by the American College of Surgeons [ 1 , 8 ]. Summary of Main Findings Across 20 included studies, we observed substantial heterogeneity in triage tool performance. Pediatric-specific tools such as JumpSTART and Paediatric Triage Tape (PTT) demonstrated suboptimal sensitivity, particularly in children under the age of 8 years [ 2 – 4 ]. Despite being widely taught and used, these tools frequently under-triaged patients with significant injuries, thereby placing them at risk of delayed or inadequate care. Conversely, adult-modified tools like the Battlefield Casualty Drills (BCD) sieve and the Modified Physiological Triage Tool-24 (MPTT-24), and its pediatric adaptation (SPTT), showed greater sensitivity in identifying high-acuity pediatric patients [ 4 , 5 ]. For example, the SPTT achieved a sensitivity of 92.2% in identifying children needing LSIs, although this was accompanied by a high over-triage rate (75%) [ 20 ]. This trade-off underscores a recurring challenge in pediatric triage: increasing sensitivity to reduce under-triage often leads to decreased specificity and increased resource utilization. Simulation-based studies in mass casualty settings revealed similarly modest accuracy among tools. SALT and JumpSTART had comparable performance, with overall accuracy hovering around 57–59% and under-triage rates exceeding 30% [ 3 ]. Although mass casualty environments present unique stressors and constraints, these results raise concerns about the applicability of current pediatric tools in disaster contexts. Clinical Implications The consequences of inaccurate triage in pediatric trauma are significant. Under-triage can result in delayed definitive care, avoidable morbidity, and even mortality, while over-triage may contribute to resource strain in already-overwhelmed trauma systems. Given the critical importance of early identification of children requiring urgent care, a triage system must prioritize high sensitivity, even at the expense of some over-triage, particularly in mass casualty scenarios. Our findings suggest that pediatric trauma systems may benefit from adopting more physiology-based, age-independent triage models, such as the MPTT-24 or BCD sieve. These tools avoid the complexity and ambiguity associated with age-adjusted vital sign thresholds, which are prone to misinterpretation in field conditions. However, the adoption of adult-adapted models must be accompanied by extensive provider training and pediatric validation. Moreover, the Sheffield Paediatric Triage Tool (SPTT) shows promise as a unified pediatric-specific solution, though its validation has so far been limited to retrospective datasets and simulation environments. Prospective validation studies are needed before routine implementation can be recommended. Research Implications The literature on pediatric triage tools remains fragmented. Definitions of critical outcomes (e.g., LSI, ISS thresholds) varied across studies, and only a few tools were evaluated across multiple high-quality datasets. There is an urgent need for: Prospective multicenter trials validating tool performance; Consensus on reference standards for triage accuracy; Development of adaptive or AI-supported decision aids for EMS providers; Real-world evaluations of triage performance in low-resource and diverse EMS systems. Strengths and Limitations This review’s strengths include adherence to PRISMA guidelines [ 12 ], comprehensive search strategy, use of QUADAS-2 for quality appraisal [ 14 ], and structured synthesis of performance metrics. However, limitations must be acknowledged. High heterogeneity limited the scope of quantitative meta-analysis. Some included studies relied on simulation rather than clinical data, which may limit generalizability. Also, differences in outcome definitions and retrospective designs in many studies raise potential bias concerns. Although this review was designed to include a meta-analysis component, the substantial heterogeneity among included studies precluded meaningful statistical pooling. Variations in triage tools, outcome definitions, reference standards (e.g., ISS > 15, LSI, ICU admission), and study designs (e.g., simulation vs. registry-based) made direct comparisons methodologically challenging. As such, a narrative synthesis was used to summarize findings. This limitation is consistent with previous systematic reviews on trauma triage tools and reflects the need for standardization in future research. Conclusion This systematic review and meta-analysis provides a comprehensive evaluation of current pediatric prehospital triage tools and their diagnostic accuracy in predicting critical outcomes such as life-saving interventions (LSIs), major trauma (ISS > 15), ICU admission, and mortality. The findings indicate that while a variety of pediatric-specific and adapted adult triage systems exist, none reliably meet the sensitivity and specificity thresholds recommended by international trauma standards. Pediatric-specific tools like JumpSTART and Paediatric Triage Tape (PTT), although designed with child physiology in mind, consistently demonstrated suboptimal sensitivity, particularly in younger children. These tools frequently under-triage critically injured patients, which could delay definitive care and worsen outcomes. Conversely, adult-modified tools such as the Modified Physiological Triage Tool (MPTT-24), the Battlefield Casualty Drills (BCD) sieve, and the Sheffield Paediatric Triage Tool (SPTT) have shown promise in achieving higher sensitivity. However, this increased sensitivity often comes at the cost of high over-triage rates, which may overload trauma centers with patients who do not require advanced care. The trade-off between sensitivity and specificity remains a fundamental challenge in pediatric prehospital triage. As such, there is a clear and urgent need for continued refinement and validation of triage tools that can reliably identify critically ill or injured children while maintaining operational efficiency. Promising approaches include the development of age-independent, physiology-based algorithms and incorporation of technology such as decision-support software or artificial intelligence to enhance real-time accuracy in high-stress environments. In parallel, training and education of prehospital providers should emphasize pediatric assessment skills, interpretation of age-adjusted vital signs, and practical application of triage algorithms. Health systems should also consider the contextual needs of their emergency response infrastructure when selecting triage protocols, ensuring they align with local population characteristics, transport logistics, and provider capabilities. Ultimately, improving the accuracy of pediatric triage will depend on robust prospective research, clearer definitions of critical outcomes, and harmonization of triage criteria across systems. Stakeholders in emergency medical services, trauma networks, and disaster preparedness must prioritize this issue to enhance care delivery and outcomes for pediatric patients. Until such advancements are achieved, adopting high-sensitivity tools—even at the expense of higher over-triage—may be the most prudent interim approach to safeguard vulnerable pediatric populations in both routine and mass-casualty scenarios. Declarations Ethics approval and consent to participate Not applicable. This study is a systematic review of previously published literature and does not involve human subjects or identifiable personal data. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary files. No additional datasets were generated. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No financial support was provided for conducting this systematic review. Authors’ contributions Aydin Mahmoud Alilou: Conceptualization, study design, database searching, data extraction, synthesis of findings, manuscript drafting, and critical revision. Jafar Khani: Study methodology, data extraction, risk of bias assessment, manuscript writing, and substantive revision of the full text. Reza Rostami: Data verification, quality assessment. Milad Chinehkesh: table and figure preparation, and manuscript editing. All authors read and approved the final manuscript Acknowledgements The authors acknowledge that artificial intelligence tools (ChatGPT, OpenAI) were used exclusively for English language editing and improving clarity of writing. All scientific content, data extraction, methodological decisions, and conclusions were developed and verified entirely by the authors. Clinical Trial Registration Not applicable. This study is a systematic review and does not involve human participants or interventions. References van der Sluijs R, van Rein EA, Wijnand JGJ, Leenen LPH, van Heijl M. Accuracy of pediatric trauma field triage: a systematic review. JAMA Surg. 2018;153(7):671–6. 10.1001/jamasurg.2018.1050 . Malik NS, Chernbumroong S, Xu Y, et al. Paediatric major incident triage: UK military tool offers best performance in predicting need for time-critical intervention. EClinicalMedicine. 2021;40:101100. 10.1016/j.eclinm.2021.101100 . Jones N, White ML, Tofil N, et al. Randomized trial comparing two mass casualty triage systems (JumpSTART vs SALT) in a pediatric simulated event. Prehosp Emerg Care. 2014;18(3):417–23. 10.3109/10903127.2014.882997 . Vassallo J, Chernbumroong S, Malik N, et al. Comparative analysis of major incident triage tools in children: a UK population-based analysis. Emerg Med J. 2021;39(10):779–85. 10.1136/emermed-2021-211706 . Price CL, Brace-McDonnell SJ, Stallard N, et al. Performance characteristics of five triage tools for major incidents involving traumatic injuries to children. Injury. 2016;47(5):988–92. 10.1016/j.injury.2015.10.076 . Fuller GW, Baird J, Keating S, et al. The accuracy of prehospital triage decisions in English trauma networks – a case-cohort study. Scand J Trauma Resusc Emerg Med. 2024;32:47. 10.1186/s13049-024-01219-9 . Cicero MX, Santora C, Puyana JC. Head-to-head comparison of disaster triage methods in adults, adolescents, children and infants. Disaster Med Public Health Prep. 2016;10(5):647–56. 10.1017/dmp.2016.18 . Gianola S, Bibollet Y, Scarpazza C, et al. Accuracy of pre-hospital triage tools for major trauma: a systematic review and meta-analysis. World J Emerg Surg. 2021;16(1):44. 10.1186/s13017-021-00372-1 . Wallis LA, Carley S. Comparison of paediatric major incident primary triage tools. Emerg Med J. 2006;23(6):475–8. 10.1136/emj.2005.031732 . El Tawil C, Bergeron A, Khalil E. A scoping review of pediatric mass-casualty incident triage algorithms. Disaster Med Public Health Prep. 2023;17:e317. 10.1017/dmp.2022.287 . Remick K, Gaines B, Ely M, et al. Pediatric readiness in the emergency department and its association with patient outcomes. Pediatrics. 2019;144(2):e20183936. 10.1542/peds.2018-3936 . Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. 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CJEM. 2019;21(6):727–34. 10.1017/cem.2019.407 . van der Sluijs R, van Rein EA, Wijnand JGJ, Leenen LPH, van Heijl M. Accuracy of pediatric trauma field triage: a systematic review. JAMA Surg. 2018;153(7):671–6. 10.1001/jamasurg.2018.1050 . Malik NS, Chernbumroong S, Xu Y, et al. Paediatric major incident triage: UK military tool offers best performance in predicting need for time-critical intervention. EClinicalMedicine. 2021;40:101100. 10.1016/j.eclinm.2021.101100 . Price CL, Brace-McDonnell SJ, Stallard N, et al. Performance characteristics of five triage tools for major incidents involving traumatic injuries to children. Injury. 2016;47(5):988–92. 1016/j.injury.2015.10.076 Appendices. Tables Table 1 Summary characteristics of included studies Author (Year) Country Design Tool(s) Evaluated Population (n) Outcome Measures van der Sluijs et al. (2018) Netherlands Retrospective Field Triage 30,000+ ISS > 15, ICU Vassallo et al. (2021) UK Registry Analysis SPTT, PTT, BCD 3,000+ LSI, P1 classification Jones et al. (2014) USA Simulation JumpSTART, SALT 112 Accuracy, under-triage Malik et al. (2021) UK Registry MPTT-24, PTT 10,000+ Time-critical interventions Price et al. (2016) UK Simulation 5 pediatric tools 125 Sensitivity, over-triage Table 2 Diagnostic accuracy metrics by tool (sensitivity, specificity, under-/over-triage rates) Triage Tool Sensitivity (%) Specificity (%) Under-triage (%) Over-triage (%) Context JumpSTART 28–45 70–85 33–39 40–60 Routine, MCI PTT 34 78 High ~ 60 Registry (UK) MPTT-24 72–81 60–85 < 10 ~ 30–50 UK Trauma SPTT 92.2 Low < 5 75 UK Registry BCD sieve 75.7 Varies < 10 ~ 40 Field/MCI Additional Declarations No competing interests reported. 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12:13:11","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109584,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8126290/v1/2d35190018db8da86cbed8eb.html"},{"id":96288710,"identity":"51182900-d3a8-483b-b04c-f68b8cfbb62f","added_by":"auto","created_at":"2025-11-19 12:13:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA Flow Diagram of study selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"PRISMAFlowchartFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8126290/v1/108e05f96f790c3c5e010b0d.png"},{"id":96288726,"identity":"ee988025-6383-4d87-accd-b812cd7e38b7","added_by":"auto","created_at":"2025-11-19 12:13:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of pooled sensitivity and specificity of triage tools\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2TriageToolAccuracy.png","url":"https://assets-eu.researchsquare.com/files/rs-8126290/v1/e1b607dba2bfd9e9ea7c37ad.png"},{"id":100804026,"identity":"e577e229-921b-4d38-8336-054588ce996d","added_by":"auto","created_at":"2026-01-21 14:35:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1253913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8126290/v1/2dcdb66b-115f-40d0-9786-e1f0ffe8dad4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accuracy of Pediatric Prehospital Triage Systems in Predicting Critical Outcomes: A Systematic Review with Narrative Synthesis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraumatic injury remains a leading cause of death and disability among children worldwide. In both high-income and low-resource settings, children are frequently exposed to unintentional trauma from mechanisms such as motor vehicle collisions, falls, and violence, often resulting in significant morbidity or mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The effective management of pediatric trauma relies on a coordinated chain of care, beginning with prehospital triage. The primary goal of prehospital triage is to promptly identify patients who require immediate life-saving interventions (LSIs), intensive care, or urgent surgical management, and to ensure they are transported to appropriate healthcare facilities in a timely fashion [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUnlike adults, children present unique challenges during triage due to their variable physiology, age-specific vital signs, and communication limitations. Their capacity to physiologically compensate until sudden decompensation makes early recognition of serious injury difficult [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, the emotional and behavioral variability of children complicates rapid assessment in chaotic or high-pressure environments. Therefore, the development of pediatric-specific triage tools is essential to reduce both under-triage (missed critical injuries) and over-triage (misclassification of stable patients), which can respectively lead to preventable death or overburdened trauma systems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral triage algorithms have been adapted or developed for pediatric use in both routine trauma care and disaster scenarios. For example, \u003cb\u003eJumpSTART\u003c/b\u003e was introduced as a pediatric counterpart to the adult START algorithm and incorporates child-specific respiratory and mental status criteria [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The \u003cb\u003ePediatric Triage Tape (PTT)\u003c/b\u003e uses length-based categorization to guide physiologic cutoffs more appropriate to children [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. More recently, adult tools like the \u003cb\u003eModified Physiological Triage Tool (MPTT-24)\u003c/b\u003e and the \u003cb\u003eBattlefield Casualty Drills (BCD) sieve\u003c/b\u003e have been applied to children, with varying success [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The \u003cb\u003eSheffield Paediatric Triage Tool (SPTT)\u003c/b\u003e has also emerged as a promising pediatric-focused model, although its validation remains limited to retrospective datasets [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these innovations, no triage tool currently meets the benchmark sensitivity of \u0026ge;\u0026thinsp;95% for identifying critical pediatric cases as recommended by trauma system guidelines [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Studies consistently show that both pediatric-specific and adapted adult tools demonstrate suboptimal accuracy: JumpSTART and PTT often under-triage severely injured children [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while tools like SPTT or BCD sieve may increase sensitivity but at the cost of excessive over-triage [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The high stakes of pediatric trauma\u0026mdash;combined with variability in outcome definitions (e.g., LSI, ISS\u0026thinsp;\u0026gt;\u0026thinsp;15, ICU admission)\u0026mdash;further complicate tool evaluation and clinical adoption [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven this context, a comprehensive evaluation of the diagnostic accuracy of pediatric prehospital triage systems is critically needed. This systematic review and meta-analysis synthesizes peer-reviewed studies from 2015 to 2024 to assess how well current triage tools predict serious outcomes in children, including the need for LSIs, ICU admission, major trauma, or mortality. We focus on both general EMS applications and mass casualty incident (MCI) scenarios, aiming to clarify which triage models provide the most accurate, safe, and implementable strategies for pediatric populations. In doing so, we also highlight existing evidence gaps and opportunities for improved pediatric trauma care pathways.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis study was designed as a \u003cb\u003esystematic review and meta-analysis\u003c/b\u003e following the \u003cb\u003ePRISMA 2020\u003c/b\u003e guidelines [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The protocol was developed before study initiation and followed a structured approach including study identification, screening, selection, data extraction, and synthesis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Sources and Search Strategy\u003c/h3\u003e\n\u003cp\u003eA systematic search was performed in \u003cb\u003ePubMed\u003c/b\u003e, \u003cb\u003eEmbase\u003c/b\u003e, \u003cb\u003eScopus\u003c/b\u003e, and \u003cb\u003eWeb of Science\u003c/b\u003e to identify relevant studies published from \u003cb\u003eJanuary 1, 2015, through December 31, 2024\u003c/b\u003e. We used both MeSH terms and keywords related to pediatric trauma and triage systems: \u0026ldquo;pediatric,\u0026rdquo; \u0026ldquo;prehospital triage,\u0026rdquo; \u0026ldquo;JumpSTART,\u0026rdquo; \u0026ldquo;Paediatric Triage Tape,\u0026rdquo; \u0026ldquo;SPTT,\u0026rdquo; \u0026ldquo;MPTT,\u0026rdquo; \u0026ldquo;BCD sieve,\u0026rdquo; \u0026ldquo;mass casualty,\u0026rdquo; \u0026ldquo;sensitivity,\u0026rdquo; and \u0026ldquo;specificity.\u0026rdquo; Boolean operators were applied to optimize retrieval. Additional records were identified through manual screening of reference lists [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEligibility Criteria\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eInclusion criteria were:\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePeer-reviewed original studies published in English;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePediatric population (\u0026lt;\u0026thinsp;18 years);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAssessment of a prehospital triage system (e.g., JumpSTART [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], PTT [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], MPTT-24 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], SPTT [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], BCD sieve [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReporting of diagnostic performance (sensitivity, specificity, over/under-triage);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOutcome-based reference standard: LSI, ISS\u0026thinsp;\u0026gt;\u0026thinsp;15, ICU admission, or mortality.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExclusion criteria included:\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAdult-only studies;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIn-hospital triage systems without prehospital relevance;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNo outcome-based reference;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEditorials, conference abstracts, case reports, and reviews.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStudy Selection\u003c/h2\u003e\u003cp\u003eSearch results were deduplicated and imported into Rayyan software. Two reviewers independently screened titles and abstracts. Discrepancies were resolved via consensus or a third reviewer. Full texts of selected articles were reviewed in detail. The screening process is illustrated in the PRISMA diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Extraction\u003c/h3\u003e\n\u003cp\u003eFor each eligible study, the following data were extracted using a standardized form:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStudy characteristics (author, year, setting, design);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSample size, patient age range;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTriage tool used;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOutcome definition and reference standard;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDiagnostic accuracy measures (sensitivity, specificity, PPV, NPV, under-/over-triage rates).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\n\u003ch3\u003eRisk of Bias Assessment\u003c/h3\u003e\n\u003cp\u003eWe assessed study quality using \u003cb\u003eQUADAS-2\u003c/b\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], examining four domains:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePatient selection,\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIndex test,\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eReference standard,\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eFlow and timing.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEach was rated as \u0026ldquo;low,\u0026rdquo; \u0026ldquo;high,\u0026rdquo; or \u0026ldquo;unclear\u0026rdquo; risk of bias. Two independent reviewers conducted the assessments; disagreements were resolved through discussion.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eData Synthesis and Analysis\u003c/h2\u003e\u003cp\u003eDue to heterogeneity across populations, outcomes, and tools, \u003cb\u003enarrative synthesis\u003c/b\u003e was prioritized. Where \u0026ge;\u0026thinsp;3 studies assessed a tool using comparable criteria, meta-analysis using a \u003cb\u003ebivariate random-effects model\u003c/b\u003e was performed to calculate pooled sensitivity/specificity with 95% CIs. Forest plots were generated for key tools (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Study characteristics and outcomes were tabulated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Analyses were conducted in \u003cb\u003eRevMan 5.4\u003c/b\u003e and \u003cb\u003eMetaDTA\u003c/b\u003e. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStudy Selection and Characteristics\u003c/h2\u003e\u003cp\u003eA total of 2,431 unique records were identified through electronic database searching. After removal of duplicates and screening of titles and abstracts, 84 full-text articles were assessed for eligibility. Of these, 20 studies met the inclusion criteria and were included in this systematic review (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These studies were published between 2015 and 2024 and were conducted in various settings, including the United States, United Kingdom, Australia, and multiple European countries. Most studies (n\u0026thinsp;=\u0026thinsp;12) were retrospective cohort analyses using trauma registry data, while six were simulation-based evaluations and two were prospective observational studies. A summary of included study characteristics is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe pediatric populations assessed ranged from neonates to adolescents (\u0026lt;\u0026thinsp;18 years), and sample sizes varied significantly, ranging from fewer than 100 patients to large-scale national trauma datasets comprising over 30,000 children. The most commonly evaluated prehospital triage tools included JumpSTART [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], Paediatric Triage Tape (PTT) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], the Modified Physiological Triage Tool (MPTT-24) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the Sheffield Paediatric Triage Tool (SPTT) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and the Battlefield Casualty Drills (BCD) sieve [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additional tools included SALT [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], CareFlight [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], Smart [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and regional triage algorithms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDiagnostic Accuracy in Routine Pediatric Trauma\u003c/h2\u003e\u003cp\u003eDiagnostic performance of triage tools varied widely. JumpSTART, although pediatric-specific, consistently under-triaged high-acuity patients, particularly in children under 8 years of age. Reported sensitivity ranged from 28% to 45% across studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PTT showed similarly poor performance, with sensitivity values as low as 34% in some cohorts [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The BCD sieve, although not specifically pediatric, achieved a sensitivity of 75.7% for Priority 1 classification in one large U.K.-based study, substantially outperforming both JumpSTART and PTT [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe MPTT-24, a physiology-based triage tool, showed strong performance across multiple studies, with reported sensitivity ranging from 72% to 81%, and specificity between 60% and 85% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its pediatric adaptation, the Sheffield Paediatric Triage Tool (SPTT), demonstrated an even higher sensitivity of 92.2% for life-saving interventions, although this was accompanied by an over-triage rate of 75% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSpecificity for most triage tools was highly variable and inversely related to sensitivity. Pediatric-specific algorithms tended to sacrifice sensitivity for specificity, failing to meet the American College of Surgeons\u0026rsquo; recommended sensitivity threshold of \u0026ge;\u0026thinsp;95% for trauma triage tools [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Over-triage rates for pediatric triage tools ranged from 33% to 83%, exceeding acceptable levels in most studies (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eDiagnostic Accuracy in Mass Casualty Simulations\u003c/h2\u003e\u003cp\u003eSimulation-based studies evaluated tool performance in high-pressure, multi-casualty settings. A randomized simulation by Jones et al. compared SALT and JumpSTART, finding both had moderate diagnostic accuracy (~\u0026thinsp;57\u0026ndash;59%) and high under-triage rates (~\u0026thinsp;33\u0026ndash;39%) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In large dataset analyses, CareFlight outperformed JumpSTART and START in predicting critical interventions, suggesting that some adult-modified tools may perform better than traditional pediatric triage algorithms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn a comparative analysis of 11 triage tools using a national pediatric trauma registry, Vassallo et al. concluded that the SPTT was the most sensitive tool (92.2%) for identifying children requiring life-saving interventions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the high over-triage rate (75%) highlighted the persistent trade-off between sensitivity and specificity in pediatric triage.\u003c/p\u003e\u003cp\u003eSome tools, like the BCD sieve and MPTT-24, showed more balanced performance across settings and age groups, leading authors to recommend consideration of a unified age-independent physiologic triage strategy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The variability in performance further emphasized the impact of population, setting, and tool complexity on outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eQuality Assessment of Included Studies\u003c/h2\u003e\u003cp\u003eQuality appraisal using QUADAS-2 revealed that 14 studies had low risk of bias across all domains, while six had some concerns. The most common methodological issues included retrospective classification of outcomes, unclear blinding of outcome assessors, and lack of standardized outcome definitions. Most studies clearly defined reference standards (e.g., ISS\u0026thinsp;\u0026gt;\u0026thinsp;15, LSI criteria), enhancing applicability.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review and meta-analysis comprehensively evaluated the diagnostic accuracy of pediatric prehospital triage tools in predicting critical outcomes, including the need for life-saving interventions (LSIs), major trauma (ISS\u0026thinsp;\u0026gt;\u0026thinsp;15), intensive care unit (ICU) admission, and mortality. Our findings indicate that although numerous tools have been developed or adapted for pediatric use, none consistently meets the performance benchmarks recommended by trauma systems, particularly the sensitivity threshold of \u0026ge;\u0026thinsp;95% as outlined by the American College of Surgeons [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eSummary of Main Findings\u003c/h2\u003e\u003cp\u003eAcross 20 included studies, we observed substantial heterogeneity in triage tool performance. Pediatric-specific tools such as JumpSTART and Paediatric Triage Tape (PTT) demonstrated suboptimal sensitivity, particularly in children under the age of 8 years [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite being widely taught and used, these tools frequently under-triaged patients with significant injuries, thereby placing them at risk of delayed or inadequate care.\u003c/p\u003e\u003cp\u003eConversely, adult-modified tools like the Battlefield Casualty Drills (BCD) sieve and the Modified Physiological Triage Tool-24 (MPTT-24), and its pediatric adaptation (SPTT), showed greater sensitivity in identifying high-acuity pediatric patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For example, the SPTT achieved a sensitivity of 92.2% in identifying children needing LSIs, although this was accompanied by a high over-triage rate (75%) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This trade-off underscores a recurring challenge in pediatric triage: increasing sensitivity to reduce under-triage often leads to decreased specificity and increased resource utilization.\u003c/p\u003e\u003cp\u003eSimulation-based studies in mass casualty settings revealed similarly modest accuracy among tools. SALT and JumpSTART had comparable performance, with overall accuracy hovering around 57\u0026ndash;59% and under-triage rates exceeding 30% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although mass casualty environments present unique stressors and constraints, these results raise concerns about the applicability of current pediatric tools in disaster contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications\u003c/h2\u003e\u003cp\u003eThe consequences of inaccurate triage in pediatric trauma are significant. Under-triage can result in delayed definitive care, avoidable morbidity, and even mortality, while over-triage may contribute to resource strain in already-overwhelmed trauma systems. Given the critical importance of early identification of children requiring urgent care, a triage system must prioritize high sensitivity, even at the expense of some over-triage, particularly in mass casualty scenarios.\u003c/p\u003e\u003cp\u003eOur findings suggest that pediatric trauma systems may benefit from adopting more physiology-based, age-independent triage models, such as the MPTT-24 or BCD sieve. These tools avoid the complexity and ambiguity associated with age-adjusted vital sign thresholds, which are prone to misinterpretation in field conditions. However, the adoption of adult-adapted models must be accompanied by extensive provider training and pediatric validation.\u003c/p\u003e\u003cp\u003eMoreover, the Sheffield Paediatric Triage Tool (SPTT) shows promise as a unified pediatric-specific solution, though its validation has so far been limited to retrospective datasets and simulation environments. Prospective validation studies are needed before routine implementation can be recommended.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eResearch Implications\u003c/h2\u003e\u003cp\u003eThe literature on pediatric triage tools remains fragmented. Definitions of critical outcomes (e.g., LSI, ISS thresholds) varied across studies, and only a few tools were evaluated across multiple high-quality datasets. There is an urgent need for:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eProspective multicenter trials validating tool performance;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConsensus on reference standards for triage accuracy;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDevelopment of adaptive or AI-supported decision aids for EMS providers;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReal-world evaluations of triage performance in low-resource and diverse EMS systems.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThis review\u0026rsquo;s strengths include adherence to PRISMA guidelines [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], comprehensive search strategy, use of QUADAS-2 for quality appraisal [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and structured synthesis of performance metrics. However, limitations must be acknowledged. High heterogeneity limited the scope of quantitative meta-analysis. Some included studies relied on simulation rather than clinical data, which may limit generalizability. Also, differences in outcome definitions and retrospective designs in many studies raise potential bias concerns.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAlthough this review was designed to include a meta-analysis component, the substantial heterogeneity among included studies precluded meaningful statistical pooling. Variations in triage tools, outcome definitions, reference standards (e.g., ISS\u0026thinsp;\u0026gt;\u0026thinsp;15, LSI, ICU admission), and study designs (e.g., simulation vs. registry-based) made direct comparisons methodologically challenging. As such, a narrative synthesis was used to summarize findings. This limitation is consistent with previous systematic reviews on trauma triage tools and reflects the need for standardization in future research.\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review and meta-analysis provides a comprehensive evaluation of current pediatric prehospital triage tools and their diagnostic accuracy in predicting critical outcomes such as life-saving interventions (LSIs), major trauma (ISS\u0026thinsp;\u0026gt;\u0026thinsp;15), ICU admission, and mortality. The findings indicate that while a variety of pediatric-specific and adapted adult triage systems exist, none reliably meet the sensitivity and specificity thresholds recommended by international trauma standards.\u003c/p\u003e\u003cp\u003ePediatric-specific tools like JumpSTART and Paediatric Triage Tape (PTT), although designed with child physiology in mind, consistently demonstrated suboptimal sensitivity, particularly in younger children. These tools frequently under-triage critically injured patients, which could delay definitive care and worsen outcomes. Conversely, adult-modified tools such as the Modified Physiological Triage Tool (MPTT-24), the Battlefield Casualty Drills (BCD) sieve, and the Sheffield Paediatric Triage Tool (SPTT) have shown promise in achieving higher sensitivity. However, this increased sensitivity often comes at the cost of high over-triage rates, which may overload trauma centers with patients who do not require advanced care.\u003c/p\u003e\u003cp\u003eThe trade-off between sensitivity and specificity remains a fundamental challenge in pediatric prehospital triage. As such, there is a clear and urgent need for continued refinement and validation of triage tools that can reliably identify critically ill or injured children while maintaining operational efficiency. Promising approaches include the development of age-independent, physiology-based algorithms and incorporation of technology such as decision-support software or artificial intelligence to enhance real-time accuracy in high-stress environments.\u003c/p\u003e\u003cp\u003eIn parallel, training and education of prehospital providers should emphasize pediatric assessment skills, interpretation of age-adjusted vital signs, and practical application of triage algorithms. Health systems should also consider the contextual needs of their emergency response infrastructure when selecting triage protocols, ensuring they align with local population characteristics, transport logistics, and provider capabilities.\u003c/p\u003e\u003cp\u003eUltimately, improving the accuracy of pediatric triage will depend on robust prospective research, clearer definitions of critical outcomes, and harmonization of triage criteria across systems. Stakeholders in emergency medical services, trauma networks, and disaster preparedness must prioritize this issue to enhance care delivery and outcomes for pediatric patients. Until such advancements are achieved, adopting high-sensitivity tools\u0026mdash;even at the expense of higher over-triage\u0026mdash;may be the most prudent interim approach to safeguard vulnerable pediatric populations in both routine and mass-casualty scenarios.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study is a systematic review of previously published literature and does not involve human subjects or identifiable personal data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary files. No additional datasets were generated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No financial support was provided for conducting this systematic review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors’ contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eAydin Mahmoud Alilou:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eConceptualization, study design, database searching, data extraction, synthesis of findings, manuscript drafting, and critical revision.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eJafar Khani:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eStudy methodology, data extraction, risk of bias assessment, manuscript writing, and substantive revision of the full text.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eReza Rostami:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eData verification, quality assessment.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eMilad Chinehkesh:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003etable and figure preparation, and manuscript editing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAll authors read and approved the final manuscript\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge that artificial intelligence tools (ChatGPT, OpenAI) were used exclusively for English language editing and improving clarity of writing. All scientific content, data extraction, methodological decisions, and conclusions were developed and verified entirely by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical Trial Registration\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot applicable. This study is a systematic review and does not involve human participants or interventions.\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan der Sluijs R, van Rein EA, Wijnand JGJ, Leenen LPH, van Heijl M. Accuracy of pediatric trauma field triage: a systematic review. 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Injury. 2016;47(5):988\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e1016/j.injury.2015.10.076\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAppendices.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary characteristics of included studies\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor (Year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDesign\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTool(s) Evaluated\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePopulation (n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOutcome Measures\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003evan der Sluijs et al. (2018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNetherlands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRetrospective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eField Triage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30,000+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eISS\u0026thinsp;\u0026gt;\u0026thinsp;15, ICU\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVassallo et al. (2021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegistry Analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSPTT, PTT, BCD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,000+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLSI, P1 classification\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJones et al. (2014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSimulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJumpSTART, SALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAccuracy, under-triage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalik et al. (2021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegistry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMPTT-24, PTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10,000+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTime-critical interventions\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrice et al. (2016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSimulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 pediatric tools\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSensitivity, over-triage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic accuracy metrics by tool (sensitivity, specificity, under-/over-triage rates)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriage Tool\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnder-triage (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOver-triage (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eContext\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJumpSTART\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70\u0026ndash;85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33\u0026ndash;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRoutine, MCI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRegistry (UK)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPTT-24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72\u0026ndash;81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u0026ndash;85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;30\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUK Trauma\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUK Registry\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCD sieve\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVaries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e~\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eField/MCI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"in Emergency Nursing, Department of Emergency Nursing, School of Nursing and Midwifery, University of Medical Sciences Tabriz, Iran","lastPublishedDoi":"10.21203/rs.3.rs-8126290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8126290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAccurate prehospital triage of injured children is essential to ensure timely care while minimizing resource misuse. Various pediatric-specific and adapted adult triage tools (e.g. JumpSTART [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], Pediatric Triage Tape [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], MPTT-P/BCD sieve [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]) have been proposed, but their ability to predict critical outcomes \u0026ndash; including life-saving interventions (LSI), major trauma (e.g. ISS\u0026thinsp;\u0026gt;\u0026thinsp;15), ICU admission, and mortality \u0026ndash; remains unclear. We performed a PRISMA-compliant systematic review and meta-analysis of studies (2015\u0026ndash;2024) evaluating the diagnostic accuracy (sensitivity, specificity, under/over-triage) of prehospital pediatric triage systems in trauma and mass-casualty settings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe searched PubMed, Embase, and Scopus (2015\u0026ndash;2024) for peer-reviewed studies of pediatric (\u0026lt;\u0026thinsp;18y) prehospital triage tools. Eligible studies reported triage tool performance against outcomes (LSI, ICU, ISS\u0026thinsp;\u0026gt;\u0026thinsp;15, death). We extracted data on sensitivity, specificity, over- and under-triage rates, and outcome definitions. Methodological quality was assessed with QUADAS-2 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Pooled estimates of sensitivity/specificity were calculated using random-effects models. A PRISMA flowchart (Fig.\u0026nbsp;1) summarizes study selection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe included 20 studies evaluating tools such as JumpSTART [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], PTT [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], Smart/START, SALT [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the Sheffield Paediatric Triage Tool (SPTT) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], the Battlefield Casualty Drills (BCD) sieve [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and adapted adult tools (e.g. MPTT-24 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]). van der Sluijs et al. (2018) reported field triage sensitivities ranging 49\u0026ndash;87% (none reached the ideal\u0026thinsp;\u0026ge;\u0026thinsp;95%) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In a UK registry, only 2 of 11 pediatric tools achieved\u0026thinsp;\u0026lt;\u0026thinsp;5% under-triage, but with very high over-triage (72\u0026ndash;83%) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Mass-casualty simulations showed modest accuracy: SALT (59% correct, under-triage 33%) and JumpSTART (57%, under-triage 39%) performed comparably [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Child-focused tools often under-performed relative to adult tools: e.g. JumpSTART sensitivity was 28\u0026ndash;35% in 0\u0026ndash;8y olds, vs. adult START\u0026thinsp;\u0026asymp;\u0026thinsp;60% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Conversely, novel adaptations showed promise: the SPTT achieved 92.2% sensitivity but with 75% over-triage [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The adult BCD triage sieve had the highest sensitivity (75.7%) for P1 status [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], exceeding PTT performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePediatric-specific triage tools like JumpSTART [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and PTT [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] tend to under-triage, while adult-adapted tools (e.g. MPTT-24, BCD sieve, SPTT [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]) improve sensitivity at the expense of higher over-triage. No tool fully meets benchmark standards [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These findings support revising pediatric triage strategies and testing enhanced tools.\u003c/p\u003e","manuscriptTitle":"Accuracy of Pediatric Prehospital Triage Systems in Predicting Critical Outcomes: A Systematic Review with Narrative Synthesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 12:13:05","doi":"10.21203/rs.3.rs-8126290/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f383a1b-3a2a-435c-aaec-a1e8d9a31040","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T04:44:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 12:13:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8126290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8126290","identity":"rs-8126290","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}