Diagnoses and Clinical Outcomes Associated with Surgically Amputated Feline Digits Submitted to Multiple Veterinary Diagnostic Laboratories
Bibliographic record
Abstract
Amputation is commonly performed in an attempt to both treat and diagnose conditions affecting the digits of cats. The records of multiple veterinary diagnostic laboratories were searched to identify submissions of amputated digits from cats. Eighty-five 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. The Kaplan-Meier product-limit method was used to determine the disease-free interval and survival time. Neoplastic disease was identified in 63 of 85 submissions, with exclusively inflammatory lesions composing the other 22 cases. In 60 (95.2%) of the neoplastic cases, a malignant tumor was identified. Squamous cell carcinoma was the most commonly identified malignant tumor ( n = 15; 23.8%) and was associated with a median survival time of 73 days. Other diagnoses included fibrosarcoma ( n = 14; 22.2%); adenocarcinoma, likely metastases of a primary pulmonary neoplasm ( n = 13; 20.6%); osteosarcoma ( n = 5; 7.9%); mast cell tumor ( n = 4; 6.3%); hemangiosarcoma ( n = 5; 7.9%); malignant fibrous histiocytoma ( n = 2; 3.2%); giant cell tumor of bone ( n = 2; 3.2%); and hemangioma ( n = 2; 3.2%). Giant cell tumor of bone has not been previously described in the digits of cats. Various neoplasms can occur in the digits of cats, and submission of the amputated digit for histopathologic diagnosis is essential to determine the histogenesis and predict the clinical outcome.
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".