Bibliographic record
Abstract
The causes of single or multiple cutaneous nodules are many. It is important to perform fine‐needle aspirations for cytological examination and to perform skin biopsies. In certain cases a surgical excision of the entire nodule, with a subsequent histological examination, is a simpler approach. When the nodule represents a cutaneous metastasis of a primary neoplasia and is the only sign of the tumour, the aetiology of the nodule can sometimes be rather uncommon. The diagnosis may be made more difficult when the lesion is isolated, appears benign, is in an unusual location (cranial aspect of foreleg, dorsal trunk or scapular region) or is difficult to excise due to haemostasis and local tissue infiltration. The histological interpretation can sometimes prove to be a complicated matter, requiring the use of specific markers to identify anapaestic tumour, but nonetheless is necessary in order to be able to characterize the primary tumour. Using these four examples, a description is provided of the distant localization of an ovarian dysgerminoma in an Afghan hound bitch, a mammary adenocarcinoma in a Labrador bitch, and an extraskeletal mammary osteosarcoma in a doberman bitch and a vesicular adenocarcinoma in a Briard dog. In the last two cases cited, the Alamartine–Ball–Cadiot syndrome was in its final stage of development.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".