Reticulocytosis in nonanemic dogs: increasing prevalence and potential etiologies
Bibliographic record
Abstract
BACKGROUND: An increasing prevalence of reticulocytosis in the absence of anemia (RAA) in dogs has been suspected in recent years. OBJECTIVES: The objectives were to determine whether prevalence of RAA in our canine population has been increasing over the last years, and to identify potential predisposing factors. METHODS: The annual prevalence of RAA in adult dogs was determined between 2000 and 2012. Clinical histories and CBC data were analyzed for all dogs, as well as owner response to a questionnaire including information on nutrition and supplements was conducted for dogs with RAA identified between 2011 and 2012. In addition, serum iron concentration (Fe), total iron-binding capacity (TIBC), and percent transferrin saturation (%TS) were determined in 14 dogs with RAA and compared with 8 healthy control dogs. RESULTS: Reticulocytosis in the absence of anemia was identified in 1035 dogs, with the prevalence increasing since 2006. Dogs with RAA evaluated after 2006 (n = 853) had significantly lower MCV and were more likely to have microcytosis than those prior to 2006 (n = 182). Increased incidence of osteoarthritis was observed in dogs evaluated after 2006, including the dogs studied between 2011 and 2012 (n = 31), and administration of nonsteroidal anti-inflammatory drugs, omega-3 fatty acids, and glucosamine was more common in the latter. Significantly lower mean Fe and %TS, and higher TIBC were found in dogs with RAA compared to unaffected dogs. CONCLUSIONS: Prevalence of RAA has increased in recent years in our canine population. More ubiquitous use of anti-inflammatory medications and nutraceuticals, associated with increased diagnosis of osteoarthritis should be considered as contributing factors.
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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 | 0.001 |
| 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 | 0.000 | 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".