Bibliographic record
Abstract
Canine infective endocarditis is defined as an infection of the endocardium, commonly involving one or more heart valves and leading to proliferative (vegetative) or erosive lesions. Prerequisites for development of infective endocarditis include endocardial damage, formation of a sterile vegetative lesion or coagulum, presence of bacteraemia and microorganism adherence to the coagulum. Predisposing factors include any that may facilitate meeting the prerequisites, including congenital heart disease, extra-oral infections and immunosuppression, amongst others. Large, male, middle-aged and purebred dogs may be overrepresented, in particular Labrador Retrievers, Golden Retrievers, Boxers and German Shepherd Dogs. Typically caused by bacteria, the most common isolates include Streptococcus spp., Staphylococcus spp., Gram-negative rods (particularly Escherichia coli) and Bartonella spp. Blood culture can have low sensitivity, with up to 70% of cultures being negative. Bartonella spp. are increasingly being recognised as a cause of culture-negative aortic infective endocarditis. The preferred method of diagnosis in vivo is echocardiography, with a reported sensitivity of 87.5%. The prognosis is guarded despite appropriate treatment, and some negative prognostic factors have been identified.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".