A Field-Side Triage Model for Early Specialist Referral After Acute Lower Extremity Sports Injuries in Young Athletes: Development and Internal Validation

This study developed and internally validated a high-performing field-side triage model using readily available clinical variables to effectively stratify risk and guide early specialist referral decisions for young athletes with acute lower extremity sports injuries.

The Gist

Imagine you are standing on the sidelines of a high school soccer game. A player goes down hard, clutching their knee. The coach, the athletic trainer, and maybe a parent are there, but there's no MRI machine, no X-ray, and no orthopedic surgeon in sight.

The big question is: "Do we need to call the specialist right now, or can we just ice it and watch?"

If you call the specialist for every scraped knee, you clog up the system and waste money. If you wait too long on a torn ligament, the player might miss their whole season or suffer permanent damage. Getting this balance right is like trying to guess the weather with just a glance at the sky—you need a reliable shortcut.

This paper is about building that shortcut.

The Problem: Guessing in the Dark

In the world of sports medicine, especially for young athletes (under 22), doctors often have to make tough calls with very little information. Usually, they rely on their "gut feeling" or years of experience. But just like how two people can look at the same clouds and disagree on whether it will rain, different coaches and trainers might make different decisions for the same injury. This leads to confusion and inconsistent care.

The Solution: A "Traffic Light" System for Injuries

The researchers (doctors from Ashiya Central Hospital in Japan) looked back at over 2,000 young athletes who got hurt between 2017 and 2025. They wanted to see which injuries eventually required surgery. Surgery is a good "red flag" because if a player needs an operation, they definitely needed to see a specialist immediately.

They built a prediction model—think of it as a smart, digital traffic light system for injuries.

Instead of needing complex tests, this system only asks five simple questions that anyone on the sidelines can answer in seconds:

1. How old are they? (Is the athlete older or younger than 15?)
2. Are they male or female?
3. Where did it hurt? (Is it the knee or somewhere else?)
4. How bad is the function? (Can they walk? Can they keep playing? Or are they stuck on the ground?)
5. How did it happen? (Was it a high-speed crash or a simple twist?)

How the "Traffic Light" Works

Based on the answers, the model sorts the athlete into one of three zones:

• 🟢 Green Light (Low Risk): The injury is likely minor. The athlete can probably be treated with rest and ice. No need to panic or rush to the surgeon.
• 🟡 Yellow Light (Intermediate Risk): It's a bit tricky. Keep a close eye on them, maybe get a second opinion, but don't panic yet.
• 🔴 Red Light (High Risk): The odds are high that this athlete needs surgery. Call the specialist immediately.

The Results: It Works Like a Charm

The researchers tested their model, and it was surprisingly accurate.

• The "Full" Model: If you know all the details (including exactly how the injury happened), the model is about 89% accurate at predicting who needs surgery.
• The "Simplified" Model: Even if you don't know the exact mechanism of the injury (which is common in the heat of the moment), the model is still 88% accurate.

This is huge. It means a coach or trainer doesn't need to be a detective to figure out if a player needs a surgeon. They just need to look at the player's age, gender, where it hurts, and if the player can walk.

Why This Matters

Think of this model as a spell-checker for medical decisions.

• Before: A trainer might think, "Oh, it's just a knee sprain," and send the kid home, only for the kid to come back next week with a torn ACL.
• After: The trainer uses the model, sees the "Red Light" (because the kid is female, over 15, and can't walk), and immediately says, "We need a specialist."

The Bottom Line

This study gives us a simple, easy-to-use tool that helps non-experts make expert-level decisions. It doesn't replace the doctor; it just helps the people on the field know when to call the doctor.

By using a simple "traffic light" system based on five easy facts, we can ensure that young athletes get the right help at the right time, avoiding both unnecessary panic and dangerous delays. It's a small change in how we ask questions, but it could save a lot of seasons and careers.

Technical Summary

1. Problem Statement

In the management of acute sports injuries among young athletes (≤22 years), there is a critical need for timely specialist referral. However, initial assessments often occur in resource-limited environments (e.g., field-side) where diagnostic tools like imaging are unavailable.

• The Challenge: Current decision-making relies heavily on subjective judgment and varying levels of clinical experience, leading to inconsistent triage.
• The Gap: Existing clinical decision rules (e.g., Ottawa Ankle/Knee Rules) are designed primarily to guide imaging for fracture detection, not to guide specialist referral for a broad range of injuries that may require surgical intervention.
• Objective: To develop and internally validate a practical, field-side prediction model using only readily obtainable clinical variables to stratify risk and support early referral decisions for young athletes with acute lower extremity injuries.

2. Methodology

Study Design & Population:

• Design: Retrospective cohort study conducted at a single-center sports medicine clinic in Japan.
• Participants: 2,129 athletes aged ≤22 years presenting with acute lower extremity sports injuries (thigh, knee, lower leg, ankle, or foot) between January 2017 and November 2025.
• Outcome Measure: Surgical intervention was selected as the primary outcome. It serves as an objective, clinically meaningful surrogate for injuries requiring specialist evaluation.

Predictor Variables:
Variables were selected based on clinical relevance and availability during immediate post-injury assessment:

1. Age: Dichotomized at 15 years (≥15 vs. <15).
2. Sex: Male vs. Female.
3. Injury Site: Knee vs. Non-knee.
4. Functional Severity: A 3-grade scale based on immediate activity limitation:
   • Grade 1: Can continue sports immediately.
   • Grade 2: Cannot continue sports but can walk independently.
   • Grade 3: Cannot bear weight or walk independently.
5. Injury Mechanism: High-Energy Deceleration (HED) vs. Non-HED (e.g., landing, cutting, rapid deceleration involving substantial load).

Model Development:

• Full Model: Included all five predictors (Age, Sex, Site, Severity, Mechanism).
• Simplified Model: Excluded "Injury Mechanism" (as this is often hard to ascertain immediately) and treated Functional Severity as an ordinal variable to enhance field-side applicability.
• Statistical Approach: Univariable and multivariable logistic regression were used. Internal validation was performed using bootstrap resampling (1,000 iterations) to correct for overfitting.

Performance Metrics:

• Discrimination: Area Under the Curve (AUC).
• Calibration: Brier score, calibration plots, and calibration slope.
• Clinical Utility: Decision Curve Analysis (DCA) to compare net benefit against "treat-all" and "treat-none" strategies.
• Risk Stratification: Probabilities were categorized into Low (<5%), Intermediate (5–15%), and High (>15%) risk groups based on predefined thresholds.

3. Key Results

Demographics & Outcomes:

• Total cohort: 2,129 athletes.
• Surgical rate: 13.0% (276 athletes).
• Higher surgical rates were observed in athletes aged ≥15, females, those with knee injuries, and those with higher functional severity.

Predictors:
In multivariable analysis, the following were independent predictors of surgical intervention:

• Older age (≥15 years)
• Female sex
• Knee involvement
• Greater functional severity (Grade 3 being the highest risk)
• High-Energy Deceleration (HED) mechanism

Model Performance:

• Full Model: AUC 0.890; Brier score 0.073; Calibration slope 1.00.
• Simplified Model: AUC 0.883; Brier score 0.075.
• Conclusion: The simplified model (excluding mechanism) demonstrated high discrimination comparable to the full model, confirming that complex mechanism data is not strictly necessary for accurate triage.

Risk Stratification & Utility:

• High-Risk Group (>15% probability): Surgical rate of 38.6%.
  • Sensitivity: 81.9%
  • Specificity: 80.6%
  • Negative Predictive Value (NPV): 96.8%
  • Number Needed to Refer (NNR): ~2.6 (referring 3 high-risk athletes identifies ~1 surgical case).
• Decision Curve Analysis: The model provided a greater net benefit than "treat-all" or "treat-none" strategies across clinically relevant probability thresholds (5%–20%).

4. Key Contributions

1. Referral-Oriented Framework: Unlike existing rules focused on imaging for fractures, this is the first model specifically designed to guide specialist referral for surgical intervention in young athletes.
2. Field-Side Applicability: The development of a simplified model relying solely on observable variables (Age, Sex, Site, Functional Status) allows for immediate risk stratification without waiting for imaging or detailed biomechanical analysis.
3. Staged Assessment: The study proposes a tiered approach where initial triage uses basic variables, with the option to refine risk if mechanism data becomes available later.
4. Standardization: Provides a structured, objective tool to reduce reliance on subjective judgment by non-specialist providers (coaches, trainers) in the field.

5. Significance and Limitations

Significance:

• Clinical Impact: Enables early identification of athletes at high risk for surgical intervention, potentially reducing delays in treatment, preventing complications, and optimizing return-to-play timelines.
• Resource Optimization: Helps avoid unnecessary specialist referrals for low-risk injuries (Low-risk group had only a 1.7% surgical rate), thereby reducing healthcare costs and specialist workload.
• Accessibility: Makes expert-level risk assessment accessible to non-specialists in resource-limited settings.

Limitations:

• Single-Center Design: Conducted at one hospital in Japan; external validation in diverse populations and settings is required.
• Retrospective Nature: Prospective validation is needed to confirm performance in real-time clinical practice.
• Exclusion of Advanced Data: The model intentionally excludes imaging and detailed physical exam findings to maintain field-side utility; thus, it may be less precise in specialized clinical settings where such data is available.
• Outcome Proxy: While surgery is a strong proxy for severity, it does not capture all clinically significant injuries that might require non-surgical specialist management.

Conclusion:
The study successfully developed a robust, internally validated triage model that uses simple, immediate clinical observations to predict the need for surgical intervention in young athletes. It offers a practical tool to standardize and improve early referral decisions in sports medicine.