Research priorities in single-ventricle heart conditions: a United Kingdom national study
Bibliographic record
Abstract
OBJECTIVE: To bring together stakeholders in the United Kingdom to establish national priorities for research in single-ventricle heart conditions. METHODS: This study comprised two surveys and a workshop. The initial public online survey asked respondents up to three questions they would like answered for research. Responses were classified as unanswered, already answered, or unable to be answered by scientific research. In the follow-up survey, unanswered questions were divided into categories and respondents were asked to rank categories and questions by priority. A stakeholder workshop attended by patients, parents, healthcare professionals, researchers, and charities was held to determine the final list of research priorities. RESULTS: A total of 128 respondents posed 344 research questions, of which 271 were classified as unanswered, and after removing duplicates, 204 questions remained, which were divided into 20 categories. In the second survey, 56 (49.1%) respondents successfully ranked categories and questions. A total of 39 participants attended the workshop, drawing up a list of 30 research priorities across nine priority categories. The nine priority categories are: Associated co-morbidities; Brain & neurodevelopment; Exercise; Fontan failure; Heart function; Living with a single ventricle heart condition; Management of the well-functioning Fontan circulation; Surgery & perioperative care; and Transplantation, mechanical support & novel therapies. CONCLUSIONS: Through a multi-stage process, we engaged a wide range of interested parties to establish a list of research priorities in single-ventricle heart conditions. This provides a platform for clinicians, researchers, and funders in the United Kingdom and elsewhere to address the most important questions and improve outcomes in these rare but high-impact CHDs.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".