Serum protein electrophoresis in 147 dogs
Bibliographic record
Abstract
Reference intervals for serum protein electrophoresis (SPE) were created from a group of 75 clinically healthy dogs and compared with SPE results obtained from clinical cases presented to the University of Bristol over an eight-and-a-half-year period. A total of 147 dogs, in which SPE had been performed, had complete case records available and thus met the inclusion criteria. Signalment and final diagnoses taken from the case records and SPE results were divided into normal and abnormal based on the newly established reference intervals. Cases were grouped according to the SPE protein fraction abnormalities and diagnosis using the DAMNITV classification system. Of the 147 cases, 140 (95.2 per cent) had abnormal SPE results. The most common protein fraction abnormality was decreased albumin (59.3 per cent) followed by a polyclonal increase in γ globulins (38.6 per cent). Decreased β-1 globulins and increased β-2 globulins were documented in 36.4 and 30.0 per cent of cases, respectively. The most common DAMNITV classification associated with abnormal SPE results was infectious/inflammatory disease, which was diagnosed in 79 of 140 cases (56.4 per cent). Monoclonal gammopathies were noted in eight dogs (5.7 per cent), and underlying lymphoproliferative disease was present in all cases where a diagnosis was achieved, including multiple myeloma (four dogs), splenic plasmacytoma (one dog), hepatic plasmacytoma (one dog) and lymphoma (one dog).
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".