Validation of the Shin Pain Scoring System: A Novel Approach for Determining Tibial Bone Stress Injuries
Bibliographic record
Abstract
BACKGROUND: The incidence of adolescent overuse injuries, including bone stress injuries (BSIs), is on the rise. The identification of a BSI in the early stages is key to successful treatment. The Shin Pain Scoring System (SPSS) was developed to aid clinicians in identifying patients with a BSI. HYPOTHESIS: The SPSS will correlate with magnetic resonance imaging (MRI) grading of a BSI in an adolescent population. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: Enrolled in this study were 80 adolescent high school athletes between the ages of 13 and 18 years participating in a variety of sports with more than 1 week of atraumatic shin pain. The SPSS questionnaire was completed for each participant, and physical examination findings were recorded. Each question and physical examination item was allotted a point value, which totaled 29 points. Radiographs and MRI scans of both lower legs were obtained for each participant. The SPSS score was statistically analyzed using logistic regression, a classification matrix, and a 2 × 2 contingency table to evaluate validity and predictability. RESULTS: Logistic regression analysis of our data determined that 3 categories of SPSS scores provided the highest diagnostic value when compared with MRI grading based on the Fredericson classification (0-4). The SPSS correctly identified 43.5% of injuries for category 1 (MRI grades 0-1), 62.5% for category 2 (MRI grade 2), and 50.0% for category 3 (MRI grades 3-4). Overall, the SPSS correctly identified the degree of BSI in 54.4% of all tibias studied. Binary analysis for validity demonstrated a sensitivity of 96%, specificity of 26%, positive predictive value of 76%, and negative predictive value of 71% for the SPSS relative to the "gold standard" MRI results. CONCLUSION: The SPSS is a potentially valid method to identify tibial BSIs, given the sensitivity and negative and positive predictive values. It also provides helpful categorization to alert clinicians to the presence of a BSI and direct further diagnostics and/or interventions. The SPSS should be considered as an additional tool to use when evaluating adolescents with atraumatic tibial BSIs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".