Speed and accuracy of mobile BlackBerry Messenger to transmit chest radiography images from a small community emergency department to a geographically remote referral center
Bibliographic record
Abstract
BACKGROUND: Small emergency departments (EDs) may rely on radiologists at remote centers for interpretations of chest radiographs (CXRs). We investigated systematic transmission of CXR images from a small ED to a geographically remote referral center using the mobile BlackBerry Messenger (BBM) application. METHODS: Investigators obtained de-identified CXR images of consecutive ED patients via mobile phone camera. Each CXR image, along with a brief clinical history, was sent via BBM to an emergency physician located at a remote referral site, and the receiving physician replied via BBM to confirm reception. All communications, image generation, and image analysis was conducted on mobile phones. The primary outcome was the proportion of BBMs received within two minutes of sending; the secondary outcome was the proportion of BBM replies to the sending physician within five minutes. Image accuracy-comparing the radiologist's interpretation with the receiving emergency physician's interpretation-was estimated using predefined criteria. RESULTS: Of 1281 consecutive ED patients, 231 (18.0 %) had CXRs obtained, 320 CXRs were analyzed and 611 BBMs sent. All BBMs (100.0%, 95% confidence interval (CI) 99.4-00.0) arrived within two minutes; 595 BBMs (97.4%, 95% CI 95.8-98.4) were replied to within five minutes. Of the 58 CXRs with abnormalities requiring intervention, there were 55 concordances (overall agreement 94.2%, 95% CI 85.9-98.3; kappa 0.95, 95% CI 0.89-1.0) CONCLUSION: Systematic transmission of CXR images from a small ED to a remote large center using mobile phones may be a safe and effective strategy to rapidly communicate important patient information.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".