[PP.16.30] INTERPRESS-IPD
Bibliographic record
Abstract
Objective: Inter-arm differences in blood pressure (IAD) are associated with increased cardiovascular and all-cause mortality in cohort studies of various populations. Study level meta-analyses confirm these associations, however several questions remain, which such methods cannot answer, including: What is the independent contribution of IAD to prognostic risk estimation for cardiovascular and all-cause mortality? What minimum cut-off value for IAD defines elevated risk? What is the incremental association between increasing IAD and mortality risk? Do different IAD measurement techniques affect prognostic value of IAD measurements? We aim to address these questions by forming an international collaboration, the ‘INTERPRESS-IPD’, to combine individual patient data (IPD) from IAD cohort studies into a single large dataset for IPD meta-analysis. Design and method: The Collaboration will combine IPD from prospective cohorts that measured blood pressure in both arms during recruitment. Lead investigators contributing datasets will be ackowledged in all relevant publications. We will first develop a standardised multivariable Cox regression model using a one-stage meta-analysis of time to event data for risk of cardiovascular and all-cause mortality, taking account of IAD. The project then seeks to develop a new prognostic model for cardiovascular risk estimation that includes IAD. We will explore variations in risk contribution of IAD across pre-defined subgroups, establish the lower limit of IAD that is assocatied with additional cardiovascular risk, and plan subgroup analysis by method of IAD measurement (sequential vs. simultaneous, and single vs. repeated measures). We will perform cross-sectional analyses to describe the epidemiology of IAD in the dataset. The study is registered with PROSPERO: registration number CRD42015031227. Results: The study has secured funding from the NIHR Research for Patient Benefit programme (PB-PG-0215-36009). Searches for IAD datasets are underway and unpublished datasets are sought. Currently 12 eligible studies have been identified and of these, 9 groups have agreed to share data. On this basis the dataset will include at least 1150 all-cause deaths and 681 cardiovascular deaths in 17153 participants. Conclusions: This international collaboration will provide robust evidence on IAD to inform clinical care and future guidelines. Updates will be presented to conference.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.183 | 0.088 |
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".