Fetal diagnosis of congenital heart disease by telemedicine
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the feasibility, accuracy and user acceptability of performing remote fetal echocardiograms (FEs). SETTING: A regional fetal cardiology unit and a district general hospital (DGH). DESIGN: A prospective study over 20 months. An initial FE was performed by a radiographer in the DGH (D1) followed by a second FE transmitted to the regional centre, in real time, via a telemedicine link with live guidance by a fetal cardiologist (D2). A FE was performed later at the regional centre (D3, reference standard). Structured questionnaires were employed to evaluate the technical quality of each tele-link and the radiographers' confidence at performing FE. RESULTS: 69 remote FEs were performed and showed 58 normal hearts and 11 with congenital heart disease (CHD). D2 was accurate in 97% of cases compared with D3 (κ score=0.89) indicating excellent agreement. All tele-links connected at first attempt with a mean study time = 13.9 min. Overall tele-link quality was rated highly (median=4/5). In 94% of tele-links, at least 11/12 components of the FE were confidently assessed. The mean composite radiographer's questionnaire score increased significantly during the study period (p<0.05). CONCLUSIONS: To date this is the largest study of its kind. CHD can be confidently diagnosed and excluded by remote FE. Radiographers report increased confidence and proficiency following involvement in real-time telemedicine. This application of telemedicine could improve access to fetal cardiology and support radiographers screening for CHD.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".