Bibliographic record
Abstract
Pulmonary hemorrhage has prognostic and therapeutic implications so it is important to recognize the condition. The etiologies causing pulmonary hemorrhage are uncommon but numerous in small animals. Signalment and history can help narrow the differential diagnoses. Trauma-induced contusions are probably the most common cause of pulmonary hemorrhage in dogs and cats, with a good prognosis in most cases. Neoplasia, particularly hemangiosarcoma, should be considered in geriatric patients. Outbreaks of hemorrhagic pneumonia have been reported in groups of dogs, with bacterial infection being the most common cause. Other less common causes of pulmonary hemorrhage include coagulopathies, pulmonary thromboembolism, infectious disease (e.g. leptospirosis), and exercise-induced hemorrhage. Hemoptysis can be an important clue to the presence of pulmonary hemorrhage but may not be present in small animals (see Chapter 36). Diagnostic imaging, cytology and culture, coagulation testing, and endoscopy are often helpful in working up suspected cases of pulmonary hemorrhage.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".