Acquisition and Evaluation of Radiography Images by Digital Camera
Bibliographic record
Abstract
To determine applicability of low-cost digital imaging for different radiographic modalities used in consultations from remote areas of the Ecuadorian rainforest with limited resources, both medical and financial. Low-cost digital imaging, consisting of hand-held digital cameras, was used for image capture at a remote location. Diagnostic radiographic images were captured in Ecuador by digital camera and transmitted to a password-protected File Transfer Protocol (FTP) server at VCU Medical Center in Richmond, Virginia, using standard Internet connectivity with standard security. After capture and subsequent transfer of images via low-bandwidth Internet connections, attending radiologists in the United States compared diagnoses to those from Ecuador to evaluate quality of image transfer. Corroborative diagnoses were obtained with the digital camera images for greater than 90% of the plain film and computed tomography studies. Ultrasound (U/S) studies demonstrated only 56% corroboration. Images of radiographs captured utilizing commercially available digital cameras can provide quality sufficient for expert consultation for many plain film studies for remote, underserved areas without access to advanced modalities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".