Bibliographic record
Abstract
Cardiovascular disease generates a substantial bur-den of illness in Canada and beyond. Yet recentepidemiologic trends have been very encouraging. Deaths and rates of morbidity from cardiovascular dis-ease fell by at least 50 % in most countries from about 1980 to 2000. Some two-thirds of this decline can be at-tributed to a decrease in adverse events and reflects re-ductions in the prevalence of major risk factors. The re-maining third is attributable to reduced case-fatality rates, owing mainly to treatments.1 This victory is worth celebrating; but are we winning our long-term war against cardiovascular disease? In this issue, Tu and colleagues report on trends in cardio-vascular disease in Canada between 1994 and 2004.2 Mortal-ity due to cardiovascular disease fell by 30 % in this period. The decline was slightly more for acute myocardial infarction than for stroke and heart failure. Hospitalization rates for
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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.034 | 0.019 |
| Insufficient payload (model declined to judge) | 0.104 | 0.052 |
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".