Clinical utility of corticosteroid-induced alkaline phosphatase in adult dogs admitted to a veterinary teaching hospital
Bibliographic record
Abstract
Objective'. To examine the usefulness of corticosteroid-induced alkaline phosphatase as a diagnostic test. 'Design'. Retrospective. 'Sample population'. Data set A: Dogs over 12 months of age admitted to the Ontario Veterinary College Veterinary Teaching Hospital (OVC-VTH) between January 1, 1992, and December 31, 1997, with a total alkaline phosphatase > 2000 IU/L on presentation. Data set B: The initial biochemical profile of all dogs not born in 1997 or 1998, admitted to the OVC-VTH for the first time between January 1 and December 31, 1998, who had a biochemical profile performed. 'Procedures'. Generation of receiver operator characteristic plots to identify activities of serum absolute corticosteroid-induced alkaline phosphatase (CALP) and relative activities of CALP (%CALP) useful for diagnosing hyperadrenocorticism (HAC). Determination of other diseases and medications associated with CALP at these values using odds ratios with confidence intervals. Diagnostic comparison between CALP or %CALP and clinical signs. Modeling a dose-response relationship between cumulative prednisone dose and CALP or %CALP. 'Results'. Of medications, only corticosteroids were highly associated with CALP and %CALP in dogs with elevated alkaline phosphatase. CALP of 50% had a sensitivity of 100% for the diagnosis of HAC in all groups. Alimentary diseases were sparing for CALP. Clinical signs were as useful as measuring CALP or %CALP to diagnose HAC. No dose response relationship was evident between steroid exposure and CALP or %CALP. 'Conclusion and clinical relevance'. Clinicians can request CALP if the dog is not exposed to corticosteroids, has elevated total alkaline phosphatase, and does not have clinical signs consistent with liver disease. If CALP is <50%, hyperadrenocorticism is effectively ruled out. Routine measurement of CALP on biochemical profiles is not recommended.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".