P33 Setting research priorities for the use of digital technology in the prevention and management of heart health: the results of the James Lind priority setting partnership
Bibliographic record
Abstract
<h3>Background</h3> Patient outcomes in cardiovascular disease are characterised by inequalities in access to care and a failure to adequately address risk factors such as obesity, hypertension and physical inactivity. Digital technologies could help to address these challenges. <h3>Aim</h3> To generate a top 10 list of research priorities for the use of digital technology in the prevention and management of heart disease and heart conditions in the United Kingdom and Ireland, with equal input by people with lived experience and healthcare professionals. <h3>Methods</h3> James Lind Alliance methodology was used. An initial open response survey (completed between September and December 2023) gathered research ideas, which were filtered, categorised into summary questions, then checked against existing literature. An interim survey (completed between August and December 2024) asked respondents to select up to 10 questions that they considered most important. The top 20 ranked questions were discussed at a final workshop in December 2024. <h3>Results</h3> Ninety-nine respondents (62.2% with lived experience) submitted 422 questions. After removal of out-of-scope uncertainties and the creation of unanswered summary questions, 42 uncertainties were ranked by 133 respondents (73.7% with lived experience). The top 10 questions were agreed at the final workshop and the top three were: How can technology help people to prevent and manage a heart problem if they have one? How can technology give individualised support to help people manage their heart health? and How accurate and reliable is technology to measure and manage heart health and heart risk factors? <h3>Conclusion</h3> Future funding should be directed towards research questions identified by patients and healthcare professionals.
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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".