Bibliographic record
Abstract
An 11-year-old male castrated mixed breed dog presented to the primary veterinarian (rDVM) for lethargy, brown urine, and pale mucous membrane before he was referred to Cornell University Hospital for Animals for splenectomy. Physical examination of this patient revealed pertinent abnormalities including mildly pale mucous membranes and a palpable abdominal mass. A complete blood count (CBC) and serum chemistry revealed abnormalities consistent with the patient's previously diagnosed hypothyroidism, but no other clinically significant abnormalities. An abdominal ultrasound revealed a large irregular splenic mass. A splenectomy was performed. The whole spleen and a nodule on the omentum were submitted for histopathology. Even though hemangiosarcoma (HSA) was highly suspected, the histopathology result was consistent with a splenic hematoma and omental daughter spleens. The dog was clinically doing well three months after the surgery, but his blood work at the rDVM showed mild anemia and hypoalbuminemia. No further diagnostics were pursued at this point. Using this case as a framework, the characteristic presentation of splenic HSA, common diagnostic findings, and long-term outcome of patients with splenic hematoma after splenectomy are reviewed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".