Predicting Surgical Versus Nonsurgical Management of Acute Isolated Distal Radius Fractures in Patients Under Age 60 Using a Convolutional Neural Network
Bibliographic record
Abstract
PURPOSE: Distal radius fractures (DRFs) represent up to 20% of the fractures in the emergency department. Delays to surgery of more than 14 days are associated with poorer functional outcomes and increased health care utilization/costs. At our institution, the average time to surgery is more than 19 days because of the separation of surgical and nonsurgical care pathways and a lengthy referral process. To address this challenge, we aimed to create a convolutional neural network (CNN) capable of automating DRF x-ray analysis and triaging. We hypothesize that this model will accurately predict whether an acute isolated DRF fracture in a patient under the age of 60 years will be treated surgically or nonsurgically at our institution based on the radiographic input. METHODS: We included 163 patients under the age of 60 years who presented to the emergency department between 2018 and 2023 with an acute isolated DRF and who were referred for clinical follow-up. Radiographs taken within 4 weeks of injury were collected in posterior-anterior and lateral views and then preprocessed for model training. The surgeons' decision to treat surgically or nonsurgically at our institution was the reference standard for assessing the model prediction accuracy. RESULTS: We included 723 radiographic posterior-anterior and lateral pairs (385 surgical and 338 nonsurgical) for model training. The best-performing model (seven CNN layers, one fully connected layer, an image input size of 256 × 256 pixels, and a 1.5× weighting for volarly displaced fractures) achieved 88% accuracy and 100% sensitivity. Values for true positive (100%), true negative (72.7%), false positive (27.3%), and false negative (0%) were calculated. CONCLUSIONS: After training based on institution-specific indications, a CNN-based algorithm can predict with 88% accuracy whether treatment of an acute isolated DRF in a patient under the age of 60 years will be treated surgically or nonsurgically. CLINICAL RELEVANCE: By promptly identifying patients who would benefit from expedited surgical treatment pathways, this model can reduce times for referral.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".