Babesiosis Caused by a Large <i>Babesia</i> Species in 7 Immunocompromised Dogs
Bibliographic record
Abstract
BACKGROUND: A large unnamed Babesia species was detected in a dog with lymphoma. It was unknown if this was an underrecognized pathogen. OBJECTIVE: Report the historical and clinicopathologic findings in 7 dogs with babesiosis caused by a large unnamed Babesia species characterize the 18S ribosomal ribonucleic acid (rRNA) genes. ANIMALS: Seven immunocompromised dogs from which the Babesia was isolated. METHODS: Retrospective case review. Cases were identified by a diagnostic laboratory, the attending clinicians were contacted and the medical records were reviewed. The Babesia sp. 18S rRNA genes were amplified and sequenced. RESULTS: Six of 7 dogs had been splenectomized; the remaining dog was receiving oncolytic drugs. Lethargy, anorexia, fever, and pigmenturia were reported in 6/7, 6/7, 4/7, and 3/7 dogs. Laboratory findings included mild anemia (7/7) and severe thrombocytopenia (6/7). Polymerase chain reaction (PCR) assays used to detect Babesia sensu stricto species were all positive, but specific PCR assays for Babesia canis and Babesia gibsoni were negative in all dogs. The 18S rRNA gene sequences were determined to be identical to a large unnamed Babesia sp. previously isolated. Cross-reactive antibodies against other Babesia spp. were not always detectable. Five dogs were treated with imidocarb dipropionate and 1 dog with atovaquone/azithromycin; some favorable responses were noted. The remaining dog was untreated and remained a clinically stable carrier. CONCLUSIONS AND CLINICAL IMPORTANCE: Dogs with pigmenturia, anemia, and thrombocytopenia should be tested for Babesia sp. by PCR. Serology is not sufficient for diagnosis of this Babesia sp. Asplenia, chemotherapy, or both might represent risk factors for persistent infection, illness, or both.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".