Neuroradiology Using Secure Mobile Device Review
Bibliographic record
Abstract
BACKGROUND: Image review on computer-based workstations has made film-based review outdated. Despite advances in technology, the lack of portability of digital workstations creates an inherent disadvantage. As such, we sought to determine if the quality of image review on a handheld device is adequate for routine clinical use. METHODS: Six CT/CTA cases and six MR/MRA cases were independently reviewed by three neuroradiologists in varying environments: high and low ambient light using a handheld device and on a traditional imaging workstation in ideal conditions. On first review (using a handheld device in high ambient light), a preliminary diagnosis for each case was made. Upon changes in review conditions, neuroradiologists were asked if any additional features were seen that changed their initial diagnoses. Reviewers were also asked to comment on overall clinical quality and if the handheld display was of acceptable quality for image review. RESULTS: After the initial CT review in high ambient light, additional findings were reported in 2 of 18 instances on subsequent reviews. Similarly, additional findings were identified in 4 of 18 instances after the initial MR review in high ambient lighting. Only one of these six additional findings contributed to the diagnosis made on the initial preliminary review. CONCLUSIONS: Use of a handheld device for image review is of adequate diagnostic quality based on image contrast, sharpness of structures, visible artefacts and overall display quality. Although reviewers were comfortable with using this technology, a handheld device with a larger screen may be diagnostically superior.
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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".