Evaluation of Serum Ferritin as a Tumor Marker for Canine Histiocytic Sarcoma
Bibliographic record
Abstract
BACKGROUND: Canine histiocytic sarcoma (HS) is an aggressive malignancy. Hyperferritinemia has been documented in dogs with HS and could serve as a tumor marker aiding in diagnosis and treatment. In people, hyperferritinemia is found in inflammatory diseases, liver disease, and hemolysis, and thus may occur in dogs with these conditions. OBJECTIVE: To determine if serum ferritin concentration is a tumor marker for canine HS. ANIMALS: Dogs with HS (18), inflammatory diseases (20), liver disease (24), immune-mediated hemolytic anemia (IMHA) (15), and lymphoma (23). METHODS: Prospective, observational, cohort study: Serum ferritin concentration was measured at initial diagnosis. Parametric methods were used to compare mean log ferritin concentrations among disease categories. Receiver-operating characteristic curves and likelihood ratios were used to evaluate serum ferritin concentration as a tumor marker. RESULTS: Varying proportions of dogs with IMHA (94%), HS (89%), liver disease (79%), lymphoma (65%), and inflammatory diseases (40%) had hyperferritinemia. Dogs with IMHA had significantly higher mean ferritin concentration than dogs in all other categories. Dogs with HS had significantly higher mean ferritin concentration than those in the inflammatory disease and lymphoma categories. Mean serum ferritin concentration was not significantly different between dogs with HS and those with liver disease. Decision thresholds were determined to distinguish IMHA and HS from the other diseases associated with hyperferritinemia. CONCLUSION: Hyperferritinemia is common in dogs with HS and, after IMHA is ruled out, the degree of hyperferritinemia may be useful in differentiating dogs with HS from dogs with inflammatory diseases, liver disease, and lymphoma.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".