Why this work is in the frame
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Bibliographic record
Abstract
PHOTOS 1-5: Neoplasia of the skin and subcutaneous tissues are the most common tumours affecting dogs and there are several different types of neoplasia found in skin. Skin tumours are usually classified histologically as there are so many cutaneous structures that could be involved. Tumours are classified according to the tissue of origin and the level of malignancy. Mast cell tumours are the most common type of cutaneous tumour found in dogs. Schnauzers, Boston terriers and Labrador retrievers are some of the breeds that are predisposed to mast cell tumours. Cutaneous mast cell tumours arise from mast cells in the dermis and subcutaneous tissues. These tumours are often infiltrative and metastasize easily to bone marrow and other organs. Undifferentiated mast cell tumours are big, rapidly growing, ulcerated lesions. Hair loss and erythema are common. Mast cell tumours are diagnosed via fine needle aspiration cytology. Rowmanovsky and rapid haematologic type stains are used. Treatment is via surgical excision, radiation therapy or external beam radiotherapy. Poorly differentiated, metastatic mast cell tumours are fatal if there is not effective post surgical treatment. PHOTO 6: Eyelid neoplasms are common in older dogs and are usually benign. Eyelid masses can be resected but the eyelid structure must be restored after excision to maintain long-term ocular surface health. If the structure of the eyelid is damaged it may cause corneal exposure, irritation and ulceration. The most common eyelid neoplasms are sebaceous gland tumours, papillomas and melanomas. Papillomas are more common in younger dogs. Ocular papillomas may have papovavirus aetiology. Eyelid masses that are rapidly growing and ulcerated or are associated with corneal irritation should be resected as soon as possible. Histologically malignant eyelid tumours rarely metastasize and most eyelid tumours are histologically benign. Prognosis for most eyelid tumours is good as metastasis is rare and recurrence rates are low.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it