Idiopathic cervical abscesses in dogs follow seasonal trends and geographic risk factors, predominantly affecting young, large breeds: a case-control study of 67 dogs in New England
Bibliographic record
Abstract
OBJECTIVE: Describe and identify risk factors and spatiotemporal trends associated with the development of idiopathic cervical abscesses (CAs) in dogs in the Cape Cod region. The authors hypothesized that CAs without an external communicating tract would have a seasonal trend and would not be associated with identifiable foreign material. ANIMALS: 67 dogs. METHODS: Retrospective observational case-control study enrolling dogs between 2016 and 2020. Compiled patient information included age, breed, sex, weight, date of presentation, and ZIP code. Statistical evaluation was performed by use of multivariate logistic regression modeling and spatiotemporal analyses with government-compiled land cover data. Control dogs were randomly selected in a 3:1 ratio to cases. RESULTS: 67 cases were investigated. The final multivariable logistic regression model found a significant seasonal effect, with most cases in September (OR, 4.80; 95% CI, 1.91 to 12.02). Spatial analyses revealed increased risk in a cluster of 13 ZIP codes, with OR of 4.40 (95% CI, 1.95 to 9.95). Weight was a risk factor; each additional kilogram was associated with an 18.8% increase in odds (OR, 1.188; 95% CI, 1.053 to 1.341) until a maximum risk at 15 kg. Younger dogs were at greater odds; for each additional year of age, the odds of having CAs were reduced by 8.9% (OR, 0.911; 95% CI, 0.837 to 0.991). No foreign material inciting cause was identified in any of the 67 cases. CLINICAL SIGNIFICANCE: Idiopathic CAs should be a differential for young, large-breed dogs with cervical swelling in the Northeastern US. This study documented seasonality, geographical trends, and signalment predispositions for idiopathic CAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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 teacher head, 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".