Diagnoses and Clinical Outcomes Associated with Surgically Amputated Canine Digits Submitted to Multiple Veterinary Diagnostic Laboratories
Bibliographic record
Abstract
Amputation is commonly performed to both treat and diagnose conditions affecting the digits of dogs. Although histopathologic evaluation of these digits is routinely done, data on the prevalence and prognosis of neoplasms of the digit are scarce. The records of multiple veterinary diagnostic laboratories were searched to identify submissions of amputated digits from dogs. Four hundred twenty-eight separate submissions were reviewed for diagnosis, age, sex, limb of origin, and digits affected, and the original submitting clinics were surveyed to determine clinical outcome of the animal. No diagnosis could be agreed upon in 24 animals, and these were excluded from the study. Kaplan-Meier product-limit method was used to determine the disease-free interval and survival time. Neoplastic disease was identified in 296 of 404 submissions, with exclusively inflammatory lesions composing 108 cases. A total of 30 different neoplastic processes were identified. In 233 (77.7%) of the neoplastic cases, a malignant tumor was identified. Squamous cell carcinoma was the most commonly identified tumor (n = 109, 36.3%), and 11 of 42 dogs for which clinical follow-up information was available developed metastatic disease. Squamous cell carcinoma of the digit appears to have a greater metastatic potential than that occurring elsewhere in the body. Other common diagnoses included melanoma (n = 52, 17.3%), soft-tissue sarcoma (n = 29, 9.7%), and mast cell tumor (n = 20, 6.7%). Melanomas were associated with poor prognoses, with a median survival time of 365 days.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".