Bibliographic record
Abstract
A 12-year-old female spayed poodle mix presented to the Cornell University Hospital for Animals (CUHA) Internal Medicine service on referral for evaluation of chronic soft stool and pancreatitis. The work up revealed a mild thrombocytopenia, which progressed to moderate over the next two weeks, and a peri-aortic sublumbar mass. Based on these clinical abnormalities, an immune-mediated thrombocytopenia secondary to neoplasia was prioritized. A fine needle aspirate (FNA) of the mass with cytologic evaluation was planned but delayed pending improvement of the thrombocytopenia. Following successful medical management, the cytology of the FNA was consistent with a tumor neuroendocrine or endocrine origin. A computed tomography (CT) scan was performed for surgical planning and to check for metastasis. The mass was surgically removed and submitted for histopathology, which revealed a malignant neuroendocrine tumor. Upon further immunohistochemical staining, a diagnosis of extra-adrenal paraganglioma was made. The patient subsequently presented to the CUHA Oncology service and began metronomic chemotherapy with chlorambucil. This presentation will discuss the diagnosis, pathophysiology, and treatment of paraganglioma.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".