Detection of cardiovascular disease and cardiovascular risk factors in a changing world
Bibliographic record
Abstract
General introduction 9Examples of a changing cardiovascular landscape Chapter 2 Heart failure types across health care settings in women and men 21 Chapter 3 Atrial fibrillation: Trends in prevalence and antithrombotic prescriptions in the community 39 Detection and recognition of cardiovascular disease Chapter 4 Screening for abdominal aortic aneurysm in patients with clinically manifest vascular disease 57 Chapter 5 Sex differences in symptom presentation in acute coronary syndromes: a systematic review and meta-analysis 79 Chapter 6 Primary health care contact before referral for acute coronary syndrome 145 Cardiovascular risk factor measurement Chapter 7 Improving participation in screening for cardiovascular risk: a short report 165 Chapter 8 Blood pressure and cholesterol measurements in primary care: cross-sectional analyses in a dynamic cohort (2008-2018) 189 Chapter 9 Cardiovascular risk management after hypertensive disorders of pregnancy: a cohort study using electronic health record data 211 Chapter 10 Datamining to retrieve smoking status from electronic health records in primary care 225
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".