Evaluation of laboratory methods for improved characterization of dogs with von Willebrand disease
Bibliographic record
Abstract
This thesis describes an investigation of the potential for improved characterization of dogs with von Willebrand disease (vWD) using various laboratory methods. Citrated and EDTA blood samples were collected from 183 dogs with varying von Willebrand Factor (vWF) concentrations. vWF concentration was measured using both the Ontario Veterinary College vWF Antigen ELISA (OVC vWF:Ag) assay and an external control (Asserachrom vWF:Ag) ELISA. Collagen binding activity (vWF:CBA), platelet function using a platelet function analyzer (PFA-100), Factor VIII activity, partial thromboplastin time (PTT), and platelet counts were also determined. Agreement between the OVC vWF:Ag assay and the external control was excellent (concordance correlation = 0.89). The intea-assay coefficients of variation (4.4 and 7.9, respectively) and inter-assay coefficients of variation (15 and 5.9, respectively) were acceptable for the vWF:CBA and OVC vWF:Ag assays. Strong associations were found when the vWF:CBA and PFA-100 results were compared to vWF concentration. As such, the vWF:CBA and PFA-100 were considered to be valuable assets, in conjunction with a vWF:Ag assay, for a canine vWD diagnostic profile. Clinically significant associations were not found when Factor VIII activity, PTT, and platelet count were compared to vWF:Ag concentration, and therefore these tests were not included in the vWD diagnostic profile. A standardized questionnaire to assess bleeding history was mailed to the 183 study participants and their referring veterinarians. Of the 165 questionnaire respondents, 43.6% originated from vWD positive dogs, with only 48.6% of dogs in this group having reports of hemorrhagic signs. Hemorrhagic signs were approximately twice as prevalent in the vWD positive population compared to the vWD negative population. Although significant associations were found between the bleeding score and the vWD diagnostic profile components, a corresponding pattern was not evident in the raw data. Therefore, these assays cannot be used to predict risk of hemorrhage. The questionnaire sensitivity of 48.6% for the prediction of vWD status was insufficient for a screening tool, however, there is evidence of some discriminatory power for the identification of vWD status. At this time, the most accurate method to identify and characterize canine vWD patients is through a multi-test approach, which can be achieved with the vWD diagnostic profile.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".