Cardiovascular disease and the seeds of innovation: a call for papers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The 25th anniversary of the publication of the Scandinavian Simvastatin Survival Study (4S) is approaching. In these 25 years, the management of cardiovascular disease (CVD) risk has been revolutionised by landmark trials such as 4S, Hypertension Optimal Treatment, Steno-2 and lately by audits of performance such as the EuroASPIRE series. Many in government think that CVD is done and that we can now safely transfer the focus to cancer and dementia. However, that is not the case – we still have challenges in heart failure and acute coronary syndromes where event rates even after optimal therapy remain stubbornly high. The large trials of novel antithrombotics and new heart failure therapies will undoubtedly help fill these gaps. So what should a clinical practice journal be publishing? The great international research groups do translational science and large scale trials and increasingly meta-analyses. However, as a member of guidelines committees I know only too well that the evidence base for established therapies is often partial and weak. We rely on cohort studies and small randomised trials to try and define efficacy and safety in many niches which are too small to bother the industrial marketers but nevertheless comprise significant parts of our routine clinical practice. The academic promotions industry is too focused on grant income, large trials and impact factors at the expense of clinical science. You do not get promoted anymore for simply following your curiosity and asking simple questions. This does not build research groups within 3–5 years planning horizons. Far better to jump on the latest band-waggon – actually last week’s one, usually. Yet clinicians want answers to simple questions. Do our therapies work- and if so in which groups? Are they safe- and what are the confounders of safety analyses? What is the uptake and adherence to therapy in secondary care and primary care and how does it vary? Can you design large scale implementation programmes and if so what are the effects of giving information to clinicians or patients? Is there really added value to fancy computer programmes and web-sites? Do point-of-care assays really reduce admissions or save money as compared to laboratory systems? Do extra tests add value or just confuse? There is no drug company money in clinical research involving older drugs but many have never been tested or compared in properly designed randomised trials even of surrogate outcomes. We need data of this kind. The age of the mega-trial is coming to an end- they are simply too expensive. Modern technology offers the ability to do small trials on surrogate markers- carotid IMT, atherosclerotic plaque volume, liver fat that can actually answer questions about the clinical utility of many of the older (and newer) therapies especially when used in combination as is increasingly the case. Well-designed observational studies also have a place. It is possible with the aid of some simple regional collaboration, the use of computers and spreadsheets to establish cohort databases, plan comprehensive but relevant investigation protocols and audit their effects on surrogate outcomes or even events. It is also cheap and the information is useful to confirm the results of the larger trials or even more to show their limitations. The academic world always works in cycles. At the moment, mega-trials, meta-analyses and genetics are everything and CVD is yesterday’s news. The seeds of the next round of innovation are being sown now. The results of these small speculative studies will not get published in the major journals. They are too conservative to take the risk on innovation except where vastly expensive ‘translational’ technology is concerned. It is in journals such as the International Journal of Clinical Practice that radical approaches will first surface. As they will be poorly funded they will often be flawed and inadequate but none the less interesting to those that wish to see the future. We will endeavour to publish those studies and write editorials on their significance. None.
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.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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 it