The REliability of MObile TEchnologies for Acute Stroke Neuroimaging Data Interpretation: The REMOTE Study (S5.001)
Bibliographic record
Abstract
OBJECTIVE: To assess agreement in interpreting acute stroke head CT scan among readers from different specialties on mobile devices (iPad, iPhone), and on a standard radiology Picture Archiving and Communication System (PACs) workstation. BACKGROUND: Advances in technology allow physicians access to radiological images remotely, although the diagnostic performance of this approach for acute stroke head CT scan interpretation among different specialties is unknown. DESIGN/METHODS: 20 selected acute stroke head CTs were independently interpreted, in an intent-to-diagnose manner, by 16 readers from different specialties - neurologists, emergency medicine physicians and radiologists - on three modalities, iPad and iPhone [using ResolutionMD software (ResMD®, Calgary Scientific)] and PACs workstation. CT findings included: normal CT; any acute ischemic sign; any intracerebral hemorrhage (ICH); and any middle cerebral artery (MCA) hyperdense sign. Kappa (ĸ) statistics were used to assess the proportion of agreement beyond chance. RESULTS: Agreement for normal CT across all readers was best on the iPad (ĸ=0.29 vs. 0.19 for both iPhone and PACS); highest for neurologists (ĸ=0.42) and radiologists (ĸ=0.42) compared to emergency medicine physicians (ĸ=0.15). For any acute ischemic sign, the overall agreement was fair (PACs ĸ=0.38; iPhone ĸ=0.26; iPad ĸ=0.25). The overall agreement for presence or absence of ICH was fair (ĸ’s 0.34-0.40). Radiology reached excellent agreement in all three modalities (k’s 0.65-1.00) compared to neurologists (k’s 0.33-0.51). Overall agreement for the presence of hyperdense MCA sign was fair (k’s 0.33-.38), reaching moderate to good agreement for radiologists, which were consistently better than for neurologists (k’s 0.30-0.32). CONCLUSIONS: Among readers with different expertise, there is only fair agreement on identifying any head CT acute ischemic sign, ICH, and hyperdense MCA, independently from the modality used. However, among radiologists, good to excellent agreement in all modalities support that their remote head CT scan interpretation, using mobile devices, may have the potential to approach traditional PACs readings.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".