Research priorities in children and adults with congenital heart disease: a James Lind Alliance Priority Setting Partnership
Bibliographic record
Abstract
OBJECTIVE: To bring together patients, parents, charities and clinicians in a Priority Setting Partnership to establish national clinical priorities for research in children and adults with congenital heart disease. METHODS: The established James Lind Alliance methodology was used to identify and prioritise research on the management of congenital heart disease, focusing on diagnosis, treatment and outcomes. An initial open survey was used to gather potential uncertainties which were filtered, categorised, converted into summary questions and checked against current evidence. In a second survey, respondents identified the unanswered questions most important to them. At two final workshops, patients, parents, charities and healthcare professionals agreed the top 10 lists of priorities for child/antenatal and adult congenital heart disease research. RESULTS: 524 respondents submitted 1373 individual questions, from which 313 out of scope or duplicate questions were removed. The remaining 1060 questions were distilled into summary questions and checked against existing literature, with only three questions deemed entirely answered and removed. 250 respondents completed the child/antenatal survey (56 uncertainties) and 252 completed the adult survey (47 uncertainties). The questions ranked the highest by clinicians and non-clinicians were taken forward to consensus workshops, where two sets of top 10 research priorities were agreed. CONCLUSIONS: Through an established and equitable process, we determined national clinical priorities for congenital heart disease research. These will be taken forward by specific working groups, a national patient and public involvement group, and through the establishment of a UK and Ireland network for collaborative, multicentre clinical trials in congenital heart disease.
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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 | 0.331 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.049 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".