Anti‐thymocyte serum as part of an immunosuppressive regimen in treating haematological immune‐mediated diseases in dogs
Bibliographic record
Abstract
OBJECTIVES: To report the outcomes associated with the use of rabbit anti-dog thymocyte serum in dogs with haematological immune-mediated diseases. METHODS: Medical records from 2000 to 2016 of patients diagnosed with immune-mediated haemolytic anaemia, immune-mediated thrombocytopenia, pancytopenia and myelofibrosis were reviewed. All dogs had a severe or refractory disease and received rabbit anti-dog thymocyte serum. Lymphocyte counts were used to monitor the immediate anti-thymocyte effect of therapy; long-term patient outcome was recorded. RESULTS: A total of 10 dogs were included. All dogs except one had a notable decrease in their lymphocyte count after rabbit anti-dog thymocyte serum; four of nine had a decrease to less than 10% of the initial lymphocyte count and one dog reached 10·8%. All dogs were discharged from the hospital following their treatment. The dog with no alteration of lymphocyte count following therapy with rabbit anti-dog thymocyte serum had refractory immune mediated haemolytic anemia and was euthanised within two weeks. All other cases achieved clinical remission with immunosuppressive therapy eventually being tapered (3 of 10) or discontinued (6 of 10). CLINICAL SIGNIFICANCE: Rabbit anti-dog thymocyte serum therapy might be of interest as an adjunctive therapy in refractory immune-mediated diseases and suppressed lymphocyte counts in most dogs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".