Hypoglycemia and seizures associated with canine primary hepatic neuroendocrine carcinoma
Bibliographic record
Abstract
A 10-y-old intact male Labrador Retriever dog had a history of ataxia, inability to stand, and grand mal seizures. Complete blood count and serum biochemistry profiles revealed profound hypoglycemia, mildly increased alanine aminotransferase (ALT) activity, mild hypernatremia, and lymphopenia. The seizures could not be controlled with intravenous dextrose, diazepam, or propofol. The dog was euthanized given poor quality of life, and an autopsy was performed. Primary autopsy findings included firm hepatic masses that ranged from dark-red to tan, with the largest ~1.5 cm diameter, and pulmonary edema. Histologic examination of the hepatic masses revealed redundant, several-cell-thick cords, and packeted or acinar arrangements of polygonal cells, supported on a fibrovascular stroma. The neoplastic cells were immunopositive for insulin, synaptophysin, and neuron-specific enolase immunohistochemistry; granules in the tumor cells had an affinity for Grimelius silver stain. The histologic features, as well as the immunohistochemical staining profile, identified the neoplasm as a primary multifocal hepatic neuroendocrine carcinoma. Neuroendocrine carcinomas are rare in dogs and usually occur in the gastrointestinal or respiratory tract.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".