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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".