What is your diagnosis? Intracranial mass in a dog
Bibliographic record
Abstract
A 9-year-old, spayed female Chihuahua was presented for evaluation of acute, progressive neurologic disease. On physical examination the dog was depressed and laterally recumbent. The dog had marked neutrophilia with a toxic left shift and monocytosis. Using computed tomography with contrast enhancement a large intracranial mass lesion was identified in the rostral portion of the brain. The mass extended from the central thalamic region rostral to the cribiform plate and obliterated the lateral ventricles. A fine needle aspirate of the mass contained moderately pleomorphic polygonal cells with many intranuclear cytoplasmic pseudoinclusions (ICPs). The primary differential diagnosis was meningioma, based on cell morphology and the presence of ICPs. At necropsy, the mass was well-demarcated, unencapsulated, and densely cellular. Cells were arranged in papillary projections on fibrovascular stalks, and eosinophilic ICPs and nuclear folding were frequently seen. Cavitated areas of necrosis throughout the tumor mass were filled with intact and degenerated neutrophils. The histopathologic diagnosis was malignant papillary meningioma. ICPs are not frequently observed in Wright-stained cytologic preparations but may be found in many types of neoplasms, including meningiomas.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".