Mobile devices and their prospective future role in emergency radiology
Bibliographic record
Abstract
Mobile devices, wireless networks and software have significantly evolved since the late 1990s and are now available with sufficient computing power, speed and complexity to allow real-time interpretation of radiology studies. Emergency radiology (ER)'s time-sensitive nature would seem to be an excellent match for study interpretation using mobile devices, allowing the radiologist to read studies anywhere, at any time. While suitable for use by the radiologist outside of the hospital, or clinicians and surgeons at the bedside or in the operating room, these devices do have limitations, and regulatory approval for in-hospital diagnostic use is limited. In the ER setting, we suggest that the best use of mobile devices is to be available to consult directly with patients about their imaging findings and to the clinical team during rounds and at handover. This will bring the radiologist to the clinician and patient, helping us to better understand the patient's presentation, educate both the physician and patient and increase the visibility and value of the radiologist as a member of the clinical care team.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.001 | 0.002 |
| 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".