Telemedicine Applications for the Regional Paediatric Cardiology Service in Northern Ireland
Bibliographic record
Abstract
The regional paediatric cardiology centre in Northern Ireland has a telemedicine network linking the neonatal units of three district general hospitals using ISDN and IP-compliant equipment. We have previously reported the use of ISDN transmission at 128 kbit/s for remote echocardiography. In a series of 61 patients, a total of 59 transmitted scans were of sufficient diagnostic quality to confirm or exclude the presence of major congenital heart disease (CHD). There were three diagnostic errors (7%). Subsequently, we have examined the use of ISDN transmission at 384 kbit/s. Echocardiographic studies were carried out on 21 patients and all were followed up. Fourteen patients (67%) had CHD confirmed. There were two diagnostic errors (10%). Our experience of transmitting live echocardiographic images suggests that ISDN at 384 kbit/s provides the optimum balance between the clarity of the transmitted images and costs. We expect that IP videoconferencing will offer similar quality but at a lower running cost, and are currently assessing it in a pilot study. All sites within our network will remain capable of ISDN transmission until the efficacy and reliability of IP transmission has been demonstrated in a controlled trial. The clinical telemedicine service has led to the earlier diagnosis of and instigation of appropriate treatment 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".