Use of digital images to aid in the decision-making for acute upper extremity trauma referral
Bibliographic record
Abstract
UNLABELLED: This study evaluated the use of digital smartphone images in the decision-making for acute upper extremity trauma referrals. Surgeons (n = 15) were presented with ten upper limb trauma scenarios for consideration of immediate transfer. Based on verbal history and with additional images, participants were asked questions regarding diagnosis, injured tissues, recommended management and diagnostic and treatment confidence. Statistical analyses evaluated confidence level changes and relationships between confidence levels and independent variables. Confidence levels for diagnosis and treatment were increased with the provision of smartphone images, and this was statistically significant. The decision to transfer was changed in 22%. The photographs were more useful for amputation versus non-amputation injuries (diagnosis and treatment) and hand versus forearm injuries (diagnosis), and these differences reached statistical significance. Smartphone digital images were shown to be useful for decision-making in acute upper extremity trauma referrals. This improved communication may have implications for health cost savings and patient burden by minimizing unnecessary acute transfers. LEVEL OF EVIDENCE: Diagnostic Level III.
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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 | 0.002 |
| 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".