Image Exchange: IHE and the Evolution of Image Sharing
Bibliographic record
Abstract
The sharing of radiologic images has become a fundamental part of radiology services and is essential for delivering high-quality care. Film is quickly becoming obsolete as a means of transporting and sharing large volumes of imaging data. Image sharing has evolved from film to transportable media (eg, compact disks) to direct electronic exchange over the Internet. The latter two means of image sharing have associated work flow-related and technical challenges for which solutions are being developed. Integrating the Healthcare Enterprise (IHE) provides a standards-based approach to the development of robust, universally accepted solutions. Several IHE profiles have been developed to provide a framework for current image sharing efforts. The Philadelphia and New Jersey Health Information Exchanges and the Canada Health Infoway represent efforts to apply IHE technical profiles to facilitate the secure and confidential exchange of electronic images over the Internet. The research community is concomitantly developing solutions that solve image exchange issues that are specific to research (eg, the sharing of deidentified data) but that might also be encountered in the general population. The personal health record is a more recent development that may provide consumers with direct control over the process of sharing images electronically with their healthcare providers.
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".