iPhone-Based Teleradiology for the Diagnosis of Acute Cervico-Dorsal Spine Trauma
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility of iPhone-based teleradiology as a potential solution for the diagnosis of acute cervico-dorsal spine trauma. MATERIALS AND METHODS: We have developed a solution that allows visualization of images on the iPhone. Our system allows rapid, remote, secure, visualization of medical images without storing patient data on the iPhone. This retrospective study is comprised of cervico-dorsal computed tomogram (CT) scan examination of 75 consecutive patients having clinically suspected cervico-dorsal spine fracture. Two radiologists reviewed CT scan images on the iPhone. Computed tomogram spine scans were analyzed for vertebral body fracture and posterior elements fractures, any associated subluxation-dislocation and cord lesion. The total time taken from the launch of viewing application on the iPhone until interpretation was recorded. The results were compared with that of a diagnostic workstation monitor. Inter-rater agreement was assessed. RESULTS: The sensitivity and accuracy of detecting vertebral body fractures was 80% and 97% by both readers using the iPhone system with a perfect inter-rater agreement (kappa:1). The sensitivity and accuracy of detecting posterior elements fracture was 75% and 98% for Reader 1 and 50% and 97% for Reader 2 using the iPhone. There was good inter-rater agreement (kappa: 0.66) between both readers. No statistically significant difference was noted between time on the workstation and the iPhone system. CONCLUSION: iPhone-based teleradiology system is accurate in the diagnosis of acute cervicodorsal spinal trauma. It allows rapid, remote, secure, visualization of medical images without storing patient data on the iPhone.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".