A New System for Severity Scoring of Facial Fractures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Facial fractures are often the result of high-velocity trauma, causing skeletal disruption affecting multiple anatomic sites to varying degrees. Although several widely accepted classification systems exist, these are mostly region-specific and differ in the classification criteria used, making it impossible to uniformly and comprehensively document facial fracture patterns. Furthermore, a widely accepted system that is able to provide a final summary measure of fracture severity does not exist, making it difficult to investigate the epidemiologic data surrounding facial fracture severity. In this study, a comprehensive method for panfacial fracture documentation and severity measurement is proposed and validated through a retrospective analysis of 63 patients operated on for acute facial fracture. The severity scale was validated through statistical analysis of correlation with surrogate markers of severity (operating room procedure time and number of implants). Spearman correlation coefficients were calculated, and a statistically significant correlation was found between severity score and both number of implants and operating room procedure time (R = 0.92790 and R = 0.68157, respectively). Intraclass correlation coefficients were calculated to assess intrarater and interrater reliabilities of the severity scale and were found to be high (0.97 and 0.99, respectively). This severity scale provides a valuable, validated research tool for the investigation of facial fracture severity across patient populations, allowing for systematic evaluation of facial fracture outcomes, cost-benefit analysis, and objective analysis of the effect of specific interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it