Lymphoma in Psittacine Birds: A Histological and Immunohistochemical Assessment
Bibliographic record
Abstract
In psittacine birds, round cell neoplasms that originate from lymphocytes, plasma cells, histiocytes, or mast cells are sporadic and poorly described. The lack of morphological and immunohistochemical diagnostic criteria or grading schemes make specific diagnoses and prognoses challenging. We assessed cases of psittacine birds diagnosed with round cell neoplasia from 3 North American veterinary diagnostic laboratories to describe the diagnostic features of these tumors. For all cases, demographic data, anatomic distribution, histological features, and immunoreactivity for T (CD3) and B (Pax5 and MUM-1) cell markers were assessed using tissue microarrays and whole slide mounts. Thirty-eight psittacine birds representing 14 species were included. Tumors were mainly infiltrative and multicentric, were composed of homogenous sheets of round to polygonal cells, and commonly presented with a high mitotic count (average 21 mitoses per high-power field). Based on Pax5 immunoreactivity, B-cell lymphoma was most common (19/38 [50%]), and was significantly associated with involvement of the gastrointestinal and urogenital systems. Of the 38 cases, 6 (16%) were consistent with T-cell lymphoma, 3 (8%) with plasma cell tumor, and 3 (8%) were double-reactive for both B- and T-lymphocyte markers. This is the first study to describe morphologic and immunohistochemical features of round cell neoplasia in a large number of psittacine birds, and provides benchmark data for future studies aimed at elucidating the diagnosis and prognosis of these neoplasms. These data also provide useful information about reactivity of commercially available antibodies as lymphocyte markers in tissues of multiple psittacine species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".