Radiology on Handheld Devices: Image Display, Manipulation, and PACS Integration Issues
Bibliographic record
Abstract
Handheld personal digital assistants (PDAs) have undergone continuous and substantial improvements in hardware and graphics capabilities, making them a compelling platform for novel developments in teleradiology. The latest PDAs have processor speeds of up to 400 MHz and storage capacities of up to 80 Gbytes with memory expansion methods. A Digital Imaging and Communications in Medicine (DICOM)-compliant, vendor-independent handheld image access system was developed in which a PDA server acts as the gateway between a picture archiving and communication system (PACS) and PDAs. The system is compatible with most currently available PDA models. It is capable of both wired and wireless transfer of images and includes custom PDA software and World Wide Web interfaces that implement a variety of basic image manipulation functions. Implementation of this system, which is currently undergoing debugging and beta testing, required optimization of the user interface to efficiently display images on smaller PDA screens. The PDA server manages user work lists and implements compression and security features to accelerate transfer speeds, protect patient information, and regulate access. Although some limitations remain, PDA-based teleradiology has the potential to increase the efficiency of the radiologic work flow, increasing productivity and improving communication with referring physicians and patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".