Prognostic Factors for Cutaneous and Subcutaneous Soft Tissue Sarcomas in Dogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soft tissue sarcomas (STSs) develop from mesenchymal cells of soft tissues, and they commonly occur in the skin and subcutis of the dog. Although phenotypically diverse with frequently controversial histogenesis, STSs are considered as a group because they have similar features microscopically and clinically. Following resection, local recurrence rates are low in general but vary according to histologic grade and completeness of surgical margins. Complete margins predict nonrecurrence. Even most grade I STSs with "close" margins will not recur, but propensity for recurrence increases with grade. The frequency of metastasis has not been accurately estimated, but it is believed to be rare for grade I STSs and most likely to occur with grade III STSs. However, metastasis does not necessarily equate with poor survival. High mitotic index is prognostic for reduced survival time. Further research is needed to determine more precise estimates for recurrence rates and survival as related to completeness of surgical margins and to delineate potential differences in metastatic rate and median survival time between grades. Other potential indicators of prognosis that presently require further investigation include histologic type, tumor dimension, location, invasiveness, stage, markers of cellular proliferation, and cytogenetic profiles. Common issues limiting prognostic factor evaluation include biases from retrospective studies, small sample sizes, poor verification of metastasis, inconsistent STS classification and use of nomenclature, difficulties in differentiating STS phenotype, and diversity of the study population (stage of disease and treatment status).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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 it