Bibliographic record
Abstract
Chlamydia pneumoniae has been associated with cardiovascular disease and stroke in humans by numerous cross-sectional and case–control studies, but prospective, longitudinal studies have produced mixed results and most have not confirmed this association. However, pathological studies have established the association of C. pneumoniae antigen or DNA with atherosclerosis from surgical and autopsy vascular specimens, with odds ratio of around 20. Although in vitro and cell culture studies have supported biological plausible mechanisms for C. pneumoniae to induce or accelerate atherosclerosis, they lack the complexity of a real disease, thus limiting the scope of testing the hypothesis of causality. Animal models are more likely to mimic real disease in humans, and are critical in many conditions in the understanding of the pathogenesis, cause-andeffect relationships and subsequent approaches to treatment and prevention of diseases. Thus, animal experimentation in medical research has played a major role in our understanding of diseases. The earliest recorded use of animal models extends as far back as 500 BC, when Alcmaeon of Croton defined the function of the optic nerve by transection in a living animal. The Hippocratic treatise on the heart (circa 350 BC) discussed cutting the throat of a pig that drank colored water to study the act of swallowing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".