Development of a Surgical Difficulty Score for Open Reduction Internal Fixation of Pilon Fractures
Bibliographic record
Abstract
OBJECTIVES: To identify characteristics that contribute to surgical complexity in pilon fractures and to develop a machine learning (ML) Pilon Surgical Difficulty Score (PSDS) based on these factors. DESIGN: Retrospective cohort study. SETTING: Academic Level I trauma center. PATIENT SELECTION CRITERIA: Pilon fractures (OTA/AO Type 43) in adult patients treated with open reduction internal fixation. OUTCOMES MEASURES AND COMPARISONS: Various patient, injury, and radiological characteristics were assessed. Surgical difficulty was measured using 2 outcomes: (1) operative time and (2) perceived difficulty. Perceived difficulty was determined using the opinion of 16 fellowship-trained orthopaedic traumatologists on a 10-point scale. Significant predictors of difficulty were determined using univariate analyses. ML models were used to develop a PSDS for both operative time and surgical difficulty. RESULTS: One hundred operatively fixed pilon fractures were included. Predictors of operative time were age, OTA/AO classification, articular comminution, articular impaction, bone loss, delay to surgery, poor quality reduction, number of approaches, and number of articular fragments. Predictors of perceived difficulty included OTA/AO classification and delay to surgery. Operative time PSDS had a mean absolute error of 64 minutes and a 60-minute buffer accuracy of 59%. Perceived difficulty PSDS had a mean absolute error of 1.7 points and a 2-point buffer accuracy of 63%. CONCLUSION: ML was used to generate accurate PSDSs for operative time and difficulty for pilon fractures. Future work should aim to clinically validate these PSDSs, so they may improve patient outcomes. LEVEL OF EVIDENCE: Level III Diagnostic.
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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 | 0.004 | 0.022 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".